From 846d1f6606684c84b9159f0f7f6b795cff119d68 Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Sun, 2 Jul 2023 16:01:58 +0200 Subject: [PATCH 01/15] Update to scikit-learn==1.3.0 Signed-off-by: Xavier Dupre --- .azure-pipelines/linux-conda-CI.yml | 23 ++++++++++++++++++----- .azure-pipelines/win32-conda-CI.yml | 13 +++++++++++-- requirements.txt | 2 +- 3 files changed, 30 insertions(+), 8 deletions(-) diff --git a/.azure-pipelines/linux-conda-CI.yml b/.azure-pipelines/linux-conda-CI.yml index 6b69d60d3..0c5f44de7 100644 --- a/.azure-pipelines/linux-conda-CI.yml +++ b/.azure-pipelines/linux-conda-CI.yml @@ -14,6 +14,19 @@ jobs: strategy: matrix: + Py311-Onnx140-Rt151-Skl130: + do.bench: '0' + python.version: '3.11' # onnxruntime cannot support python 3.11 yet + numpy.version: '>=1.21.1' + scipy.version: '>=1.7.0' + onnx.version: 'onnx==1.14.0' # '-i https://test.pypi.org/simple/ onnx==1.14.0rc3' + onnx.target_opset: '' + onnxrt.version: 'onnxruntime==1.15.1' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003 + sklearn.version: '>=1.3.0' + lgbm.version: '' + onnxcc.version: '>=1.8.1' # git + run.example: '1' + Py310-Onnx140-Rt140-Skl122: do.bench: '0' python.version: '3.10' # onnxruntime cannot support python 3.11 yet @@ -22,12 +35,12 @@ jobs: onnx.version: 'onnx==1.14.0' # '-i https://test.pypi.org/simple/ onnx==1.14.0rc3' onnx.target_opset: '' onnxrt.version: 'onnxruntime==1.14.0' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003 - sklearn.version: '>=1.2.2' + sklearn.version: '==1.2.2' lgbm.version: '' onnxcc.version: '>=1.8.1' # git - run.example: '1' + run.example: '0' - Py310-Onnx130-Rt140-Skl122: + Py310-Onnx130-Rt140-Skl121: do.bench: '0' python.version: '3.10' numpy.version: '>=1.21.2' @@ -35,10 +48,10 @@ jobs: onnx.version: 'onnx==1.13.0' #'-i https://test.pypi.org/simple/ onnx==1.13.0rc1' onnx.target_opset: '' onnxrt.version: 'onnxruntime==1.14.0' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003 - sklearn.version: '>=1.2.1' + sklearn.version: '==1.2.1' lgbm.version: '' onnxcc.version: '>=1.8.1' # git - run.example: '1' + run.example: '0' Py310-Onnx130-Rt131-Skl120: do.bench: '0' diff --git a/.azure-pipelines/win32-conda-CI.yml b/.azure-pipelines/win32-conda-CI.yml index 8651a740e..9fb907347 100644 --- a/.azure-pipelines/win32-conda-CI.yml +++ b/.azure-pipelines/win32-conda-CI.yml @@ -13,6 +13,15 @@ jobs: vmImage: 'windows-latest' strategy: matrix: + Py310-Onnx140-Rt151-Skl130: + python.version: '3.11' # onnxruntime cannot support python 3.11 yet + onnx.version: 'onnx==1.14.0' # '-i https://test.pypi.org/simple/ onnx==1.14.0rc3' + onnx.target_opset: '' + numpy.version: 'numpy>=1.22.3' + scipy.version: 'scipy' + onnxrt.version: 'onnxruntime==1.15.1' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003 + onnxcc.version: 'onnxconverter-common>=1.8.1' # git+https://github.com/microsoft/onnxconverter-common.git + sklearn.version: '>=1.3.0' Py310-Onnx140-Rt140-Skl122: python.version: '3.10' # onnxruntime cannot support python 3.11 yet onnx.version: 'onnx==1.14.0' # '-i https://test.pypi.org/simple/ onnx==1.14.0rc3' @@ -21,7 +30,7 @@ jobs: scipy.version: 'scipy' onnxrt.version: 'onnxruntime==1.14.0' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003 onnxcc.version: 'onnxconverter-common>=1.8.1' # git+https://github.com/microsoft/onnxconverter-common.git - sklearn.version: '>=1.2.2' + sklearn.version: '==1.2.2' Py310-Onnx130-Rt140-Skl121: python.version: '3.10' onnx.version: 'onnx==1.13.0' #'-i https://test.pypi.org/simple/ onnx==1.12.0rc4' @@ -30,7 +39,7 @@ jobs: scipy.version: 'scipy' onnxrt.version: 'onnxruntime==1.14.0' # -i https://test.pypi.org/simple/ ort-nightly==1.11.0.dev20220311003 onnxcc.version: 'onnxconverter-common>=1.8.1' # git+https://github.com/microsoft/onnxconverter-common.git - sklearn.version: '>=1.2.2' + sklearn.version: '==1.2.2' Py310-Onnx130-Rt131-Skl120: python.version: '3.10' onnx.version: 'onnx==1.13.0' #'-i https://test.pypi.org/simple/ onnx==1.12.0rc4' diff --git a/requirements.txt b/requirements.txt index 73e29c0e9..cca532fb2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ onnx>=1.2.1 -scikit-learn>=0.19, <1.3 +scikit-learn>=0.19 onnxconverter-common>=1.7.0 From 4959c6480daa1ef90934d6809e3ac22a317f8574 Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 13:27:09 +0200 Subject: [PATCH 02/15] Fixes broken unit tests Signed-off-by: Xavier Dupre --- NOTICE | 2 +- docs/conf.py | 2 +- skl2onnx/algebra/automation.py | 31 +++++++++++++------ skl2onnx/algebra/onnx_ops.py | 5 ++- .../cross_decomposition.py | 2 +- ...test_algebra_custom_model_sub_estimator.py | 9 ------ tests/test_sklearn_double_tensor_type_cls.py | 2 +- tests/test_sklearn_glm_regressor_converter.py | 6 ++-- tests/test_sklearn_pls_regression.py | 2 +- 9 files changed, 33 insertions(+), 28 deletions(-) diff --git a/NOTICE b/NOTICE index 7887cae4c..0de1a3f52 100644 --- a/NOTICE +++ b/NOTICE @@ -1,5 +1,5 @@ sklearn-onnx - Copyright (c) 2018-2022 Microsoft Corporation + Copyright (c) 2018-2023 Microsoft Corporation This product includes software developed at The LF AI & Data Foundation (https://lfaidata.foundation/). diff --git a/docs/conf.py b/docs/conf.py index 3c515b382..c286e51dc 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -16,7 +16,7 @@ # -- Project information ----------------------------------------------------- project = 'sklearn-onnx' -copyright = '2018-2022, Microsoft' +copyright = '2018-2023, Microsoft' author = 'Microsoft' version = skl2onnx.__version__ release = version diff --git a/skl2onnx/algebra/automation.py b/skl2onnx/algebra/automation.py index c4d66eca9..f0f74a3b3 100644 --- a/skl2onnx/algebra/automation.py +++ b/skl2onnx/algebra/automation.py @@ -71,7 +71,7 @@ def render(self, **context): }} and {{sch.max_input}} inputs. {% endif %} {% for ii, inp in enumerate(sch.inputs) %} - * *{{getname(inp, ii)}}*{{format_option(inp)}}{{inp.typeStr}}: {{ + * *{{getname(inp, ii)}}*{{format_option(inp)}}{{get_type_str(inp)}}: {{ inp.description}}{% endfor %} {% endif %} @@ -82,7 +82,7 @@ def render(self, **context): }} and {{sch.max_output}} outputs. {% endif %} {% for ii, out in enumerate(sch.outputs) %} - * *{{getname(out, ii)}}*{{format_option(out)}}{{out.typeStr}}: {{ + * *{{getname(out, ii)}}*{{format_option(out)}}{{get_type_str(out)}}: {{ out.description}}{% endfor %} {% endif %} @@ -142,8 +142,21 @@ def get_rst_doc(op_name=None): def format_name_with_domain(sch): if sch.domain: return '{} ({})'.format(sch.name, sch.domain) - else: - return sch.name + return sch.name + + def get_type_str(obj): + if hasattr(obj, "type_str"): + return obj.type_str + return obj.typeStr + + def get_is_homogeneous(obj): + try: + return obj.is_homogeneous + except TypeError: + try: + return obj.isHomogeneous + except TypeError: + return False def format_option(obj): opts = [] @@ -151,12 +164,11 @@ def format_option(obj): opts.append('optional') elif OpSchema.FormalParameterOption.Variadic == obj.option: opts.append('variadic') - if getattr(obj, 'isHomogeneous', False): + if get_is_homogeneous(obj): opts.append('heterogeneous') if opts: return " (%s)" % ", ".join(opts) - else: - return "" + return "" def getconstraint(const, ii): if const.type_param_str: @@ -171,8 +183,7 @@ def getname(obj, i): name = obj.name if len(name) == 0: return str(i) - else: - return name + return name def process_documentation(doc): if doc is None: @@ -231,7 +242,7 @@ def build_doc_url(sch): format_name_with_domain=fnwd, process_documentation=process_documentation, build_doc_url=build_doc_url, - str=str) + str=str, get_type_str=get_type_str) return docs diff --git a/skl2onnx/algebra/onnx_ops.py b/skl2onnx/algebra/onnx_ops.py index afd274ee7..f00ed4667 100644 --- a/skl2onnx/algebra/onnx_ops.py +++ b/skl2onnx/algebra/onnx_ops.py @@ -156,7 +156,10 @@ def dynamic_class_creation(cache=False): def _c(obj, label, i): name = '%s%d' % (obj.name or label, i) - tys = obj.typeStr or '' + try: + tys = obj.type_str or '' + except TypeError: + tys = obj.typeStr or '' return (name, tys) for name in sorted(res): diff --git a/skl2onnx/operator_converters/cross_decomposition.py b/skl2onnx/operator_converters/cross_decomposition.py index 6f5ca3edd..c89aa9e9a 100644 --- a/skl2onnx/operator_converters/cross_decomposition.py +++ b/skl2onnx/operator_converters/cross_decomposition.py @@ -33,7 +33,7 @@ def convert_pls_regression(scope: Scope, operator: Operator, norm_x = OnnxDiv( OnnxSub(X, coefs.astype(dtype), op_version=opv), std.astype(dtype), op_version=opv) - dot = OnnxMatMul(norm_x, op.coef_.astype(dtype), + dot = OnnxMatMul(norm_x, op.coef_.T.astype(dtype), op_version=opv) pred = OnnxAdd(dot, ym.astype(dtype), op_version=opv, output_names=operator.outputs) diff --git a/tests/test_algebra_custom_model_sub_estimator.py b/tests/test_algebra_custom_model_sub_estimator.py index 2c58c514f..07b1f992f 100644 --- a/tests/test_algebra_custom_model_sub_estimator.py +++ b/tests/test_algebra_custom_model_sub_estimator.py @@ -369,12 +369,6 @@ def custom_classifier_converter(scope, operator, container): class TestCustomModelAlgebraSubEstimator(unittest.TestCase): - def setUp(self, log=False): - self.log = logging.getLogger('skl2onnx') - if log: - self.log.setLevel(logging.DEBUG) - logging.basicConfig(level=logging.DEBUG) - def check_transform(self, obj, X): self.log.debug("[check_transform------] type(obj)=%r" % type(obj)) expected = obj.transform(X) @@ -521,7 +515,4 @@ def test_custom_classifier(self): if __name__ == "__main__": - # cl = TestCustomModelAlgebraSubEstimator() - # cl.setUp(log=False) - # cl.test_custom_scaler_2() unittest.main() diff --git a/tests/test_sklearn_double_tensor_type_cls.py b/tests/test_sklearn_double_tensor_type_cls.py index 00178aa3f..86264dfb5 100644 --- a/tests/test_sklearn_double_tensor_type_cls.py +++ b/tests/test_sklearn_double_tensor_type_cls.py @@ -134,7 +134,7 @@ def test_modelsgd_64(self): @ignore_warnings(category=warnings_to_skip) def test_modelsgdlog_64(self): self._common_classifier( - [lambda: SGDClassifier(loss='log', random_state=32)], + [lambda: SGDClassifier(loss='log_loss', random_state=32)], "SGDClassifierLog") @unittest.skipIf( diff --git a/tests/test_sklearn_glm_regressor_converter.py b/tests/test_sklearn_glm_regressor_converter.py index 58fa3e56b..8df67187b 100644 --- a/tests/test_sklearn_glm_regressor_converter.py +++ b/tests/test_sklearn_glm_regressor_converter.py @@ -646,7 +646,7 @@ def test_model_ransac_regressor_default(self): def test_model_ransac_regressor_mlp(self): model, X = fit_regression_model( linear_model.RANSACRegressor( - base_estimator=MLPRegressor(solver='sgd', max_iter=20), + estimator=MLPRegressor(solver='sgd', max_iter=20), min_samples=5)) model_onnx = convert_sklearn( model, "ransac regressor", @@ -661,7 +661,7 @@ def test_model_ransac_regressor_mlp(self): def test_model_ransac_regressor_tree(self): model, X = fit_regression_model( linear_model.RANSACRegressor( - base_estimator=GradientBoostingRegressor(), + estimator=GradientBoostingRegressor(), min_samples=5)) model_onnx = convert_sklearn( model, "ransac regressor", @@ -780,7 +780,7 @@ def test_model_quantile_regressor(self): n_informative=3) y = numpy.abs(y) y = y / y.max() + 1e-5 - model = linear_model.QuantileRegressor().fit(X, y) + model = linear_model.QuantileRegressor(solver="highs").fit(X, y) model_onnx = convert_sklearn( model, "linear regression", [("input", FloatTensorType([None, X.shape[1]]))], diff --git a/tests/test_sklearn_pls_regression.py b/tests/test_sklearn_pls_regression.py index f82c71ae8..981ebd718 100644 --- a/tests/test_sklearn_pls_regression.py +++ b/tests/test_sklearn_pls_regression.py @@ -31,7 +31,7 @@ def test_model_pls_regression(self): self.assertTrue(model_onnx is not None) dump_data_and_model( X, pls2, model_onnx, methods=['predict'], - basename="SklearnPLSRegression") + basename="SklearnPLSRegression", verbose=10) def test_model_pls_regression64(self): X = numpy.array([[0., 0., 1.], [1., 0., 0.], From 3e8cb2bf2791247978953f0e2d0b248889b6222d Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 14:12:25 +0200 Subject: [PATCH 03/15] remove logging Signed-off-by: Xavier Dupre --- tests/test_algebra_custom_model_sub_estimator.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/tests/test_algebra_custom_model_sub_estimator.py b/tests/test_algebra_custom_model_sub_estimator.py index 07b1f992f..f3472b394 100644 --- a/tests/test_algebra_custom_model_sub_estimator.py +++ b/tests/test_algebra_custom_model_sub_estimator.py @@ -370,7 +370,6 @@ def custom_classifier_converter(scope, operator, container): class TestCustomModelAlgebraSubEstimator(unittest.TestCase): def check_transform(self, obj, X): - self.log.debug("[check_transform------] type(obj)=%r" % type(obj)) expected = obj.transform(X) onx = to_onnx(obj, X, target_opset=TARGET_OPSET) try: @@ -384,7 +383,6 @@ def check_transform(self, obj, X): assert_almost_equal(expected, got, decimal=5) def check_classifier(self, obj, X): - self.log.debug("[check_classifier------] type(obj)=%r" % type(obj)) expected_labels = obj.predict(X) expected_probas = obj.predict_proba(X) onx = to_onnx(obj, X, target_opset=TARGET_OPSET, From d3c07d59307f063c9130eb8c548eed6e0d1d8d66 Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 14:26:53 +0200 Subject: [PATCH 04/15] inuttest Signed-off-by: Xavier Dupre --- skl2onnx/algebra/automation.py | 4 ++-- skl2onnx/algebra/onnx_ops.py | 2 +- skl2onnx/operator_converters/cross_decomposition.py | 8 ++++++-- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/skl2onnx/algebra/automation.py b/skl2onnx/algebra/automation.py index f0f74a3b3..5bcd08053 100644 --- a/skl2onnx/algebra/automation.py +++ b/skl2onnx/algebra/automation.py @@ -152,10 +152,10 @@ def get_type_str(obj): def get_is_homogeneous(obj): try: return obj.is_homogeneous - except TypeError: + except AttributeError: try: return obj.isHomogeneous - except TypeError: + except AttributeError: return False def format_option(obj): diff --git a/skl2onnx/algebra/onnx_ops.py b/skl2onnx/algebra/onnx_ops.py index f00ed4667..26104c5c9 100644 --- a/skl2onnx/algebra/onnx_ops.py +++ b/skl2onnx/algebra/onnx_ops.py @@ -158,7 +158,7 @@ def _c(obj, label, i): name = '%s%d' % (obj.name or label, i) try: tys = obj.type_str or '' - except TypeError: + except AttributeError: tys = obj.typeStr or '' return (name, tys) diff --git a/skl2onnx/operator_converters/cross_decomposition.py b/skl2onnx/operator_converters/cross_decomposition.py index c89aa9e9a..1e4947f04 100644 --- a/skl2onnx/operator_converters/cross_decomposition.py +++ b/skl2onnx/operator_converters/cross_decomposition.py @@ -33,8 +33,12 @@ def convert_pls_regression(scope: Scope, operator: Operator, norm_x = OnnxDiv( OnnxSub(X, coefs.astype(dtype), op_version=opv), std.astype(dtype), op_version=opv) - dot = OnnxMatMul(norm_x, op.coef_.T.astype(dtype), - op_version=opv) + if hasattr(op, "set_predict_request"): + # new in 1.3 + coefs = op.coef_.T.astype(dtype) + else: + coefs = op.coef_.astype(dtype) + dot = OnnxMatMul(norm_x, coefs, op_version=opv) pred = OnnxAdd(dot, ym.astype(dtype), op_version=opv, output_names=operator.outputs) pred.add_to(scope, container) From 97db3487245c37eb74cafad4c620202b98023c1b Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 14:48:01 +0200 Subject: [PATCH 05/15] remove mlprodict Signed-off-by: Xavier Dupre --- docs/conf.py | 7 - docs/index_tutorial.rst | 3 +- docs/notebooks/images/cdist.png | Bin 58341 -> 0 bytes docs/notebooks/images/codegm.png | Bin 139593 -> 0 bytes docs/notebooks/images/double.png | Bin 98247 -> 0 bytes docs/notebooks/images/folders1.png | Bin 18623 -> 0 bytes docs/notebooks/images/folders2.png | Bin 43562 -> 0 bytes docs/notebooks/images/gm.png | Bin 63036 -> 0 bytes docs/notebooks/images/kmeans.png | Bin 84340 -> 0 bytes docs/notebooks/images/linreg.png | Bin 170991 -> 0 bytes docs/notebooks/images/numpyapi.png | Bin 150861 -> 0 bytes docs/notebooks/images/onnxops.png | Bin 70857 -> 0 bytes docs/notebooks/images/op.png | Bin 10233 -> 0 bytes docs/notebooks/images/op2onx.png | Bin 60942 -> 0 bytes docs/notebooks/images/pipecolor.png | Bin 163201 -> 0 bytes docs/notebooks/images/pipecolor_arrow.png | Bin 184265 -> 0 bytes docs/notebooks/images/pipecolorball.png | Bin 196874 -> 0 bytes docs/notebooks/images/pipedoc.png | Bin 92695 -> 0 bytes docs/notebooks/images/pipeskl.png | Bin 86615 -> 0 bytes docs/notebooks/images/sqsc.png | Bin 17633 -> 0 bytes docs/notebooks/images/stepwise.png | Bin 19070 -> 0 bytes docs/notebooks/images/treesplit.png | Bin 164314 -> 0 bytes docs/notebooks/introduction.ipynb | 4266 ----------------- docs/requirements.txt | 1 - docs/tutorial/plot_abegin_convert_pipeline.py | 7 +- docs/tutorial/plot_bbegin_measure_time.py | 6 +- docs/tutorial/plot_dbegin_options.py | 67 +- docs/tutorial/plot_dbegin_options_list.py | 20 +- docs/tutorial/plot_ebegin_float_double.py | 1 - docs/tutorial/plot_fbegin_investigate.py | 7 +- docs/tutorial/plot_gbegin_dataframe.py | 18 +- .../tutorial/plot_gbegin_transfer_learning.py | 1 - docs/tutorial/plot_gexternal_catboost.py | 10 - docs/tutorial/plot_gexternal_lightgbm.py | 11 - docs/tutorial/plot_gexternal_xgboost.py | 12 - docs/tutorial/plot_icustom_converter.py | 10 - .../plot_kcustom_converter_wrapper.py | 11 - docs/tutorial/plot_lcustom_options.py | 10 - docs/tutorial/plot_mcustom_parser.py | 16 - docs/tutorial/plot_pextend_python_runtime.py | 18 +- .../reference_implementation_svm.py | 47 - tests_onnxmltools/test_xgboost_converters.py | 2 +- 42 files changed, 38 insertions(+), 4513 deletions(-) delete mode 100644 docs/notebooks/images/cdist.png delete mode 100644 docs/notebooks/images/codegm.png delete mode 100644 docs/notebooks/images/double.png delete mode 100644 docs/notebooks/images/folders1.png delete mode 100644 docs/notebooks/images/folders2.png delete mode 100644 docs/notebooks/images/gm.png delete mode 100644 docs/notebooks/images/kmeans.png delete mode 100644 docs/notebooks/images/linreg.png delete mode 100644 docs/notebooks/images/numpyapi.png delete mode 100644 docs/notebooks/images/onnxops.png delete mode 100644 docs/notebooks/images/op.png delete mode 100644 docs/notebooks/images/op2onx.png delete mode 100644 docs/notebooks/images/pipecolor.png delete mode 100644 docs/notebooks/images/pipecolor_arrow.png delete mode 100644 docs/notebooks/images/pipecolorball.png delete mode 100644 docs/notebooks/images/pipedoc.png delete mode 100644 docs/notebooks/images/pipeskl.png delete mode 100644 docs/notebooks/images/sqsc.png delete mode 100644 docs/notebooks/images/stepwise.png delete mode 100644 docs/notebooks/images/treesplit.png delete mode 100644 docs/notebooks/introduction.ipynb diff --git a/docs/conf.py b/docs/conf.py index c286e51dc..cbad4d184 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -83,8 +83,6 @@ 'matplotlib': ('https://matplotlib.org/', None), 'mlinsights': ( 'http://www.xavierdupre.fr/app/mlinsights/helpsphinx/', None), - 'mlprodict': ( - 'http://www.xavierdupre.fr/app/mlprodict/helpsphinx/', None), 'numpy': ('https://docs.scipy.org/doc/numpy/', None), 'pyquickhelper': ( 'http://www.xavierdupre.fr/app/pyquickhelper/helpsphinx/', None), @@ -123,8 +121,6 @@ 'ImageNet': 'http://www.image-net.org/', 'LightGBM': 'https://lightgbm.readthedocs.io/en/latest/', 'lightgbm': 'https://lightgbm.readthedocs.io/en/latest/', - 'mlprodict': - 'http://www.xavierdupre.fr/app/mlprodict/helpsphinx/index.html', 'NMF': 'https://scikit-learn.org/stable/modules/generated/' 'sklearn.decomposition.NMF.html', @@ -136,9 +132,6 @@ 'ONNX ML operators': 'https://github.com/onnx/onnx/blob/master/docs/Operators-ml.md', 'onnxmltools': 'https://github.com/onnx/onnxmltools', - 'OnnxPipeline': - 'http://www.xavierdupre.fr/app/mlprodict/helpsphinx/mlprodict/' - 'sklapi/onnx_pipeline.html?highlight=onnxpipeline', 'onnxruntime': 'https://microsoft.github.io/onnxruntime/', 'openmp': 'https://en.wikipedia.org/wiki/OpenMP', 'pyinstrument': 'https://github.com/joerick/pyinstrument', diff --git a/docs/index_tutorial.rst b/docs/index_tutorial.rst index bd1a05f70..1bdcc2af5 100644 --- a/docs/index_tutorial.rst +++ b/docs/index_tutorial.rst @@ -35,12 +35,11 @@ The tutorial was tested with following version: import onnxruntime import xgboost import skl2onnx - import mlprodict import pyquickhelper mods = [numpy, scipy, sklearn, lightgbm, xgboost, onnx, onnxmltools, onnxruntime, - skl2onnx, mlprodict, pyquickhelper] + skl2onnx, pyquickhelper] mods = [(m.__name__, m.__version__) for m in mods] mx = max(len(_[0]) for _ in mods) + 1 for name, vers in sorted(mods): diff --git a/docs/notebooks/images/cdist.png b/docs/notebooks/images/cdist.png deleted file mode 100644 index f7762d963cb1bd61e4037ff25eacf7790307c3f0..0000000000000000000000000000000000000000 GIT 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z2m&6gtTH~4ke+M;f{lfq{;t)%-28k*U@*$m6k>h>nL6-M5L~_Z5 zBNlB0tM89LlC+Pm4=_nx3vY#VH2mw=?KNRGvggd+N`r}Ts~HVnWhS^B?Pa{}1)_K6d~B diff --git a/docs/notebooks/introduction.ipynb b/docs/notebooks/introduction.ipynb deleted file mode 100644 index 28fff8ffb..000000000 --- a/docs/notebooks/introduction.ipynb +++ /dev/null @@ -1,4266 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "733c0f22", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "# Introduction to sklearn-onnx\n", - "\n", - "\n", - "Convert [scikit-learn](https://scikit-learn.org/stable/) models into ONNX." - ] - }, - { - "cell_type": "markdown", - "id": "55f59da6", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**scikit-learn** is a few words." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "f0de0897", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RandomForestClassifier()" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.datasets import load_iris\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "iris = load_iris()\n", - "X, y = iris.data, iris.target\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", - "clr = RandomForestClassifier()\n", - "clr.fit(X_train, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "105e40f3", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 0, 2, 0, 2, 1, 1, 2, 0, 1, 0, 0, 1, 2, 1, 0, 2, 2, 1, 0, 2,\n", - " 2, 0, 2, 1, 0, 2, 2, 1, 0, 2, 0, 0, 0, 2, 1, 0])" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "clr.predict(X_test)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "33a0e6bf", - "metadata": { - "scrolled": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "from skl2onnx import to_onnx\n", - "clr_onnx = to_onnx(clr, X_train.astype(np.float32))" - ] - }, - { - "cell_type": "markdown", - "id": "dd71a293", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Part 1 : convert to ONNX**\n", - "\n", - "* [scikit-learn](https://scikit-learn.org/) design, [sklearn-onnx](https://onnx.ai/sklearn-onnx/) design\n", - "* Options (black list, white list)\n", - "* double, float, sparse\n", - "\n", - "**Part 2 : Custom operators and API**\n", - "\n", - "* Initial API: verbose\n", - "* Second API: much more readable\n", - "* Numpy API: python users\n", - "\n", - "**Part 3 : Challenges and tools**\n", - "\n", - "* The missing converter: FunctionTransformer\n", - "* Python Runtime for ONNX\n", - "* Writing mathematical function with ONNX such as FFT2D" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c84fbd12", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "

\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import add_notebook_menu\n", - "from IPython.display import Image\n", - "add_notebook_menu()" - ] - }, - { - "cell_type": "markdown", - "id": "bd9369a4", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Part 1: convert to ONNX\n", - "\n", - "\n", - "* [scikit-learn](https://scikit-learn.org/) design, [sklearn-onnx](https://onnx.ai/sklearn-onnx/) design\n", - " * simple example\n", - " * ``fit``, ``predict``, ``pipelines``\n", - " * parser, shape calculator, converter\n", - " * package folders\n", - "* Options (black list, white list)\n", - " * One opset, one ONNX graph\n", - " * Use custom optimized operator (CDist, Tokenizer)\n", - " * Black list an operator\n", - " * Zipmap\n", - "* double, float, sparse\n", - " * scikit-learn, lightgbm are using double, onnx does not support double for all operators\n", - " * cannot replace double by float --> unavoidable discrepancies\n", - " * scikit-learn is using dense and sparse ([OneHotEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html), [TfIdfVectorizer](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "248f6298", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "add_notebook_menu(first_level=2, keep_item=0)" - ] - }, - { - "cell_type": "markdown", - "id": "35a73c28", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### scikit-learn design: fit, predict, pipelines\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f06fcb75", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
" - ], - "text/plain": [ - " CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\n", - "0 1.42502 0.0 19.58 0.0 0.871 6.510 100.0 1.7659 5.0 403.0 \n", - "1 13.35980 0.0 18.10 0.0 0.693 5.887 94.7 1.7821 24.0 666.0 \n", - "\n", - " PTRATIO B LSTAT Y \n", - "0 14.7 364.31 7.39 23.3 \n", - "1 20.2 396.90 16.35 12.7 " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import warnings\n", - "from pandas import DataFrame\n", - "from sklearn.datasets import load_boston\n", - "from sklearn.model_selection import train_test_split\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "data = load_boston()\n", - "X, y = data.data, data.target\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", - "\n", - "dft = DataFrame(X_train, columns=data.feature_names)\n", - "dft['Y'] = y_train\n", - "dft.head(n=2)" - ] - }, - { - "cell_type": "markdown", - "id": "2c9154bc", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "The model:\n", - "* Normalizes a few continuous columns\n", - "* Transforms two columns with categories\n", - "* Merges all and trains a linear predictor" - ] - }, - { - "cell_type": "markdown", - "id": "c8beb5a8", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "**Training**" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "4d908ede", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Pipeline(steps=[('prep',\n", - " ColumnTransformer(transformers=[('scaler', StandardScaler(),\n", - " [0, 2, 4, 5, 6]),\n", - " ('cat', OneHotEncoder(),\n", - " [3, 8])])),\n", - " ('reg', LinearRegression())])" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.compose import ColumnTransformer\n", - "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n", - "from sklearn.linear_model import LinearRegression\n", - "from sklearn.pipeline import Pipeline\n", - "\n", - "model = Pipeline(steps=[\n", - " ('prep', ColumnTransformer([\n", - " ('scaler', StandardScaler(), [0, 2, 4, 5, 6]),\n", - " ('cat', OneHotEncoder(), [3, 8]),\n", - " ])),\n", - " ('reg', LinearRegression())\n", - "])\n", - "model.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "id": "995ab378", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Prediction**\n", - "\n", - "It calls method ``predict`` for each component of the pipeline" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "8035b449", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([23.03346535, 17.35208074, 18.89496436])" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.predict(X_test)[:3]" - ] - }, - { - "cell_type": "markdown", - "id": "a1837349", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Visualization**" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "7d828a83", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from jyquickhelper import RenderJsDot\n", - "from mlinsights.plotting import pipeline2dot\n", - "dot = pipeline2dot(model, X_train)\n", - "RenderJsDot(dot, height=\"40%\", width=\"40%\")" - ] - }, - { - "cell_type": "markdown", - "id": "2a3714ed", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### sklearn-onnx: parser, shape calculator, converter\n", - "\n", - "The conversion happens with one function: [to_onnx](https://onnx.ai/sklearn-onnx/api_summary.html#skl2onnx.to_onnx). It runs:\n", - "* the **parsers**: extract the **number of outputs** of every step in the pipeline\n", - "* the **shape calculators**: compute the **shapes** of every step in the pipeline\n", - "* the converters: **convert** every step in the pipeline and an equivalent ONNX graph" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "be6a9390", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ir_version: 7\n", - "producer_name: \"skl2onnx\"\n", - "producer_version: \"1.9.4.dev\"\n", - "domain: \"ai.onnx\"\n", - "model_version: 0\n", - "doc_string: \"\"\n", - "graph {\n", - " node {\n", - " input: \"X\"\n", - " input: \"column_indices\"\n", - " output: \"extracted_feature_columns\"\n", - " name: \"ArrayFeatureExtractor\"\n", - " op_type: \"ArrayFeatureExtractor\"\n", - " domain: \"ai.onnx.ml\"\n", - " }\n", - " node {\n", - " input: \"X\"\n", - " input: \"column_indices1\"\n", - " output: \"extracted_feat\n" - ] - } - ], - "source": [ - "from skl2onnx import to_onnx\n", - "from skl2onnx.common.data_types import DoubleTensorType\n", - "\n", - "model_onnx = to_onnx(model, X_train.astype(np.float64))\n", - "\n", - "print(str(model_onnx)[:400])" - ] - }, - { - "cell_type": "markdown", - "id": "83ec0b00", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Visualization**" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "1c6acf1f", - "metadata": {}, - "outputs": [], - "source": [ - "%load_ext mlprodict" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "ea76fabb", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%onnxview model_onnx --size=\"8%\"" - ] - }, - { - "cell_type": "markdown", - "id": "3e86e1fb", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**parser**\n", - "\n", - "0. The main parser calls the parser for the pipeline.\n", - "1. The pipeline parser calls the parser of every step.\n", - "2. The pipeline of step 1 (ColumnTransformer) calls the parser of every step included in it (recursively)." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "6b99698d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": { - "image/png": { - "width": 500 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image('images/pipeskl.png', width=500)" - ] - }, - { - "cell_type": "markdown", - "id": "de417b9a", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**parser signature**\n", - "\n", - "* Creates an instance of class [Operator](https://onnx.ai/sklearn-onnx/api_summary.html#skl2onnx.common._topology.Operator)\n", - "* Fills with [Variable](https://onnx.ai/sklearn-onnx/api_summary.html#skl2onnx.common._topology.Variable) as inputs\n", - "* Creates expected output [Variable](https://onnx.ai/sklearn-onnx/api_summary.html#skl2onnx.common._topology.Variable) as outputs\n", - "* Returns the outputs (= inputs for next steps)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3825ef1b", - "metadata": {}, - "outputs": [], - "source": [ - "def parser_example(\n", - " scope, # namespace (avoids creating duplicating names)\n", - " model, # any scikit-learn model,\n", - " inputs, # known inputs (Variable)\n", - " custom_parsers=None): # custom parser\n", - "\n", - " step_to_convert = scope.declare_local_operator(alias, model)\n", - "\n", - " # inputs\n", - " step_to_convert.inputs.append(inputs[0])\n", - "\n", - " # outputs\n", - " out1 = scope.declare_local_variable('prediction', FloatTensorType())\n", - " step_to_convert.outputs.append(out1)\n", - "\n", - " # ends\n", - " return step_to_convert.outputs" - ] - }, - { - "cell_type": "markdown", - "id": "4fb6163a", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Result after parsing**" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "7005f5b7", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": { - "image/png": { - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image('images/op2onx.png', width=800)" - ] - }, - { - "cell_type": "markdown", - "id": "b8da93c6", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**How many parsers?**\n", - "\n", - "* One for each kind of machine learning problem (regressor, classifier, clustering)\n", - "* One for each pipeline manipulations ([Pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html), [ColumnTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html), [UnionFeatures](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.FeatureUnion.html).\n", - "* One for each model with an option to add more results ('score_samples' in [GaussianMixture](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html#sklearn.mixture.GaussianMixture.score_samples))\n", - "* One main parser to dispatch the calls." - ] - }, - { - "cell_type": "markdown", - "id": "4a206d0a", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Topology\n", - "\n", - "Once the parsing is done. The topology handles the conversion of every `Operator`.\n", - "\n", - "The loop: [convert_topology](https://github.com/onnx/sklearn-onnx/blob/master/skl2onnx/common/_topology.py#L1067).\n", - "\n", - "* Function [to_onnx](http://onnx.ai/sklearn-onnx/api_summary.html#skl2onnx.to_onnx) creates a [Topology](http://onnx.ai/sklearn-onnx/api_summary.html#skl2onnx.common._topology.Topology)\n", - "* **Iteration 1:** The topology calls shape calculator, converter.\n", - "* **Iteration 2:** The converters may declare new operators, they call the parsers (recursively). Return to **iteration 1** until there is no new operator." - ] - }, - { - "cell_type": "markdown", - "id": "0fcd242b", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**shape calculator signature**\n", - "\n", - "The shape calculator estimates the output size. It can be left unknown if this size cannot be fully inferred (rare): ``FloatTensorType([None, None])``.\n", - "\n", - "example: [KMeans](https://github.com/onnx/sklearn-onnx/blob/master/skl2onnx/shape_calculators/k_means.py)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "1b6462d0", - "metadata": {}, - "outputs": [], - "source": [ - "def shape_calculator_example(operator):\n", - " op = operator.raw_operator # the scikit-learn model\n", - " input_type = operator.inputs[0].type.__class__\n", - " N = operator.inputs[0].get_first_dimension() # usually None for batch prediction\n", - " output_type = input_type([N, op.coef_.shape[1]])\n", - " operator.outputs[0].type = output_type" - ] - }, - { - "cell_type": "markdown", - "id": "8ef48ffc", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**converter signature**\n", - "\n", - "example: [KMeans](https://github.com/onnx/sklearn-onnx/blob/master/skl2onnx/operator_converters/k_means.py)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "260da768", - "metadata": {}, - "outputs": [], - "source": [ - "def decorrelate_transformer_converter(\n", - " scope, # namespace\n", - " operator, # (model, inputs, outputs)\n", - " container): # holds the onnx nodes\n", - " # ...\n", - " pass" - ] - }, - { - "cell_type": "markdown", - "id": "e50d0c08", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Registration**\n", - "\n", - "A converter must be registered to be called by the topology.\n", - "\n", - "Otherwise, the conversion fails by telling one step cannot be converted.\n", - "\n", - "This is **implicit** for all converters **in sklearn-onnx**. **Explicit** for all **custom converters**.\n", - "\n", - "```\n", - "update_registered_converter(\n", - " SklearnModel, # python class\n", - " \"SklearnModel\", # alias\n", - " shape_calculator, converter,\n", - " parser, # parser is optional if it is a standard model (Classifier, Regressor, ...)\n", - " options=None) # options are optional but must be declared if any)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "a2f74395", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Mapping between scikit-learn and ONNX**" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "0c695c77", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AAFQlQgIAkLFsHq/lwSqQEf7mLVcHqyifIvOsl1ASvzR2RrwVe/CdKdXJk2r1TP6gd3nV6ONWzGMRxxwhWc+Jyf+VW3Gs/P9uV+Z/b1hBwFNjZowB+jJI5vEX4R1fjaFS3YKDp2/Y/apbj6astyvBEV8VTj1QksipHKq1dSsAAACphzMRAQBIXVQRQKYjJAAAGamTt3wTrAIZ5XG3oFyqRw6cflj2M7sj58Gv1i/1wgEH9JRqRpTA3zXerURI1nPiBydcEMCqKhQ/m97CFNWulerYmfbecSVLso6/JDZ9Q23v8YT2vuhWSlHW25kafbTSrUr/VaNWbrUk+yOCGAdNpQAAAJDaqCYAAEDVIyCAbEBIAAAyziBveStYBTKSVRN4KVhFGfVU4bz55TizO177lrmVKlSjn1vx7H8uyiB6kp6TA8GJM7392T7d2fQHyvvnee2JUvUkVRA4oJJe0yJhhASU9XY6Tpu/cqv6VN/50WK3HsPe67zFrfuvQbKfZwAAgORi2gEAAABUBUICAJBRRnnL5GAVyGhWPv6NYBVlc0jEPP17e0m7PnONWLzr9xbbpkiJ+2gD7x4btI2cg7+qVL8w4pdvjEH0ZDwnB1RC+KK4ZBz/rs7Sdu81i1WJILIKQLWI17+styvVsVr8Qne37jltrC6+bbEauWYRe8dL+Y+5hqfGL73X3a0DAACkMM5MBAAgdVBFANmCkAAAZAwLB9wTrAJZ4SfeEjH3eMqxQeLrpPySlkoeRI5UY6K3RJTg332ctLWzd0xWLt87Ln+xQVevb2uOt3jX5491GzvFB953RNzeSvpv924XOWhbpY6VakUOokcpeZ+M58T2UdPdjw3U+9tFLNu924aLv9/SBvITkIzjN/u912yHHav3Ht0VcTv/mCNez1pD3YpT1tuVZv5PNP8dt+5peNpYXTRtkC4e94TOuu0Jb9/e/dnjyY8Io1R7MLnTOQAAAFQyqgkAAJAaCAggUxESAICM8Qd3CWSLz73l8WA1Ve19rJTlV7HPvK4MuXlF5+q3gf69t0j5vdzirYfz65uDzgA/Vqo7s2hQILz9bu9yv+uu4W1T5H6qSA2rQBHynv9oZ9qX+znxlHT2us2XHy7+fo8LBtWTpbzHX2RaBu852h1xu8i5/mt+6m3r1k1ZbxenL++fpPnveM9VhIZHzFHL0+Z4j8e7v0j2fqubYBABAACgijHtAAAAVY9/e5FNCAkAQEZ42VvyglUgq0zyljXBako41vt1laTB8GqlzOFe2vWRStq2lvfdUedT+We/Rzt266thZ2V720QdeO3p9Ye3d10+73bVvD7bd6JndCfy2BLiHUfkAPp+79iiKc9zYtMrFCl5b9vNLFxsnzWK7dcG1bePd40oEn0+ynP81b12/Ri3tXZN7zHUKfDuo9hc/2W9XXElPNYv779bk+74vea/2F2bv3KdofAx2X0k8n5L1ucIAAAgCQgKAACQWqgigEyWU+Bx68hQk3qVPD/5oJkD3RqAElkp45LUr8qv0wHeMi1YBbKOlUu/LVhNVEp/rpF+PpO2H6cDFRRsEL5GCYPi+16VdvRyjTOlOnnMoR8Fv2UBAEC2YS5kAOU1atQo//Luu+/2Lyvbb37zG61bt07jxo1zPZnl/PPP10033aQf/OAHrgeZgH9/kW2oJAAAae/f3kJAANms5AFEoPJ8WhgQsDnxSwoImOrH8WscAAAAAACgihEQQDbiz5IAkPYYIEW2W+QtU4JVIFXsf07a59Zj2Ts2IlTQjyoCAAAA8DHtAAAAVYuAALIB0w1kAUq0AkmSkmXJd3pLa29Z67eA7HWht8wIVhPBdANItnzvPbXXrZtq10o1TpCqh/PZL5P2fOxt85hrO7nee62GW0cR/JYFAADZirMaAZRVqkw3cPXVV7uezDJs2DCmG8gg/HuLbEVIIAvwh1UgSVJyMHGZt7QLVoGsdoy3fBasJoKQAJLOex/mXyntfdO1S3OmlPt06VMTZDF+ywIAgGzGwAWAskiFkMCf/vQn16o4a9euVc2aNdW4cWPXU3nGjRtHSCBDFP+3ln9nkS0ICWQB/rAKJElKDib+3VvOC1aBrGZ12rd5S22/FTdCAkDK47csAADIdgxeAEhUVYcEKsugQYPUrVs3XXPNNa4HSAxhPGSzau4SAJCWVrpLINvZ5O8rglUAAIAMN23aNP361792LQDZJtqABgBko6+++kotWrRwLaD8CAggmxASAIC0xqAoUIjPAwAAyA6ff/65hg8f7loAMl20AQuCAgAgrV69WkcccYRrAYnh31JkO0ICAJDWPneXAAgJAACAbPGLX/xCzB4JZBeCAgBwMKskQEgAZcE0AwAhAQBIc1+5SwDSWncJAAAAAJmHoAAAFNq8ebO2bdumZs2auR6g7AgIIBsREgAAAAAAAACANMAgBgAEbKoBoCwI2AEBQgIAAAAAAAAAkKYY7ACQDdavX69bb71VDz74oKZNm6bXXnvN79++fbt/CZQVATxkK0ICAIAy++wzqUsXadgwKT/fdSJj2Gtqr629xvZaAwAAAACqHtMOAMhGTZs21fTp03XXXXfpmmuu0ciRI1W7dm3Vr19fderU0VFHHaU//OEPbmsgOv69BAoREgCALLZhg9Sjh5STc/AyZEhmDvyPGRP98drSoIF01lnSiBHSihXuBgAAAAAApBjOegSQjbp3765du3Zp69at2rZtm3bu3KmCggLl5+f77euvv95tCRwsWkCAf0+RzQgJAEAWa9JEevVVadEiqUOHoO8Xv5A2b5YmTJByc4O+WI49VlqwQBo3rvRtU4UFAHbskO66K2iPHi3vfyaCZdUq6fLLpUcf9X40ni3Nmxdsk63sNbXX1l5je60BAAAAAKmLsyMBZLrvf//7amBn+RRTt25d3Xnnna4FxIeAALIdIQEAyHJ2Bn3btlKnTkG7V6/gjPpMZoPfJ57oGhHscV93nTR8uPTll9KvfiWtWeOuBAAAAAAghTDtAIBs07lzZ7+SQHF79+7VTTfd5FrAwfj3ETgYIQEAACJYaOKMM4L1jz6SvvoqWAcAAAAAINUQFACQTVq2bKnGjRu7VqBWrVq6+eabVa9ePdcDlI4qAgAhAQBAGe3fL334oXTLLdKNN0r5+Qf333tv0H755WCuf5ve4De/Kdw20vbt0vjx0imnBAP13/++NGdOMA1AyNYXLpQuuSQ4698WW7e+yO3WrZMeflgaMEBau1Z6+mmpdWtp2LDo9x2L/b/FIYe4RjErVgRVB2y/9riuvVb673/dlRHs/mzqBqvUYI+r+DJkSLDNli3Sn/8cPJ6lS6XXXpNOOil4DF9/HezLHuO//x1MiWD3aff9y19K33wTXB9avly68srg+WnfXrrjDum++4o+9ni2CZ/H884LjimSva7//Gfha2HHcuutwfMSae9eKS8vuK8pUyQLez/xRPDY7Db22tg2AAAAAAAAQGm6du3q1gJWWWDo0KGuBQCIFyEBAEDCbIB47lzpL38JBvb37Qv69+wJ+qdPD/p37gzm97cB6REjpAsuCEICs2cH24dsIP/666XDD5fefjs4e79NG+knP5HeeMNt5HnzTenHP5YGDZI2bQrCCDZwboGEcHDa+mbNCu7Xtnn1Vemdd6TatYN928B3SWzAeubMYL1zZ+mYY4L1SHZMP/uZvP8BkT7/XHrlFemDD6T/9/+CxxKy4MNttwXH89xzwfNjg+L160vdugVTGliAwEIAdp9/+pP0xRfBcVrbjtnua+XK4HFOmyb93/9Jv/tdcD+2PmlS8NzafRl7fHfdJV1xRfD4bV916gT7DcWzjYUC/va34HkMQwohe47GjpV+//vgWGwfdrxWecFCDYsWBdvt2BGEHaZOlZ55Rtq8Wfr1r6Xq1YOpHNq1k+6+u3B7AAAAAEDiqCYAIJtccMEFB6oGVKtWTVdddZVatGjht4Fo+DcRiI6QAAAgYd7vb3XvHpzR3qGD6/TYWffW369f0L9kifSDHwSD6XY2ug1MN28uzZsn7d4d3MYGvydPlo4/XurbV6pRIwgL2G1s4NrCBhs3Bts//3zQd+KJwTG0bCn17Cm99VbhtAB23Y9+FFQusFBBzZrBYLqdzf7ss8FtorH920D/T38aDGRfdJH0i19Ideu6DRw7Fhs4t+M74YRgwNuqBFxzjfTSS8FjCasaWIUDu08LO9j92mPr31/e/7wEz0EYbLDrbHD9nHOCQMWqVcEAvJ2p/49/SCefHGxrgQB7Do88MthXr15BaMKOxwbjzaefBlUJ7Db2HNlZ/lbxwJ7TUDzb2AD+wIHB81icVXiwsMOYMdJxxwX7sOfCggO239Gjg2CBvVYXXhg8NmPvB3verKqAvS8sQGFBifnzg+sBAAAAAGVD2WQA2aJLly4qcH98q169um6zPzABCeDfTCBQzV0CAJB03/V+b0UOyluo1yoE2BnmYfWB//0vONvfztq3AfeQhQnstjbQbgPnFkBo1Upq21Zq1sxt5ClpurEzzgjCCVbW38rz233beqSRI4O+WrWCYENYRv+vfw0GwIuzcv925ryFGiJZyX7zr38FA+XGztDfujW475Ddjw2om2jTE1gJfivhbyEACyjY/dggvA3M2yD+UUe5DT32fNlgvlmwIAg6WN/77wfPqT0WY8/l974XrJt4tonFpiN44YXgubRjjWTHYs+3VZKw56G4M8+UvvUt1/DY62vP+bZtrgMAAAAAkDScOQkgE7Vr10417awgT8+ePXVC+Ic2AEBCCAkAAKqUnXFuZ5Nb+f1wnn5bmjYNzo5fsyaYtsD67Cx0K9vfuHFQgt9K1tvZ7LHYYLgNsJfEznq38LENslsIwc6yP+ywYDA/ms8+C47LggqRx9ulS3C9hQLCOfZtugATBiJCYbAhMuwQsuO1/UWy29u0BjYdgQUHIu/Xpl4wYfDiO9+RfvjDYCoBu85CDfb4zj9fys0Nto1nm1hsqgKrCGDVDMLHF7LnzIIhZvH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- "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('images/pipecolorball.png')" - ] - }, - { - "cell_type": "markdown", - "id": "19f57d7b", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Some thoughts after playing many hours with converters**\n", - "\n", - "* The three components (parser, shape_calculator, converter) are overcomplex.\n", - "* The converter should be the only part needed.\n", - "* The code can be simplified without changing the user API.\n", - " * It needs to move the parsing code into the converter (see [_parse_sklearn_pipeline](https://github.com/onnx/sklearn-onnx/blob/master/skl2onnx/_parse.py#L242) and [convert_pipeline](https://github.com/onnx/sklearn-onnx/blob/master/skl2onnx/operator_converters/pipelines.py#L10)).\n", - " * Modify class Topology." - ] - }, - { - "cell_type": "markdown", - "id": "a4115853", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Package folders" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "c664cd3b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ry2i3FVNZZa0nqvuK2Z86Maufi6+WHp2gy3Wfvti9JX1mT5nYw4wrMbZSZWWV0RdyesbpjGuKqShBPX07iSd/rGkQl9rPSa2vI/qksX8DvZI5Kf1io8vURp0PAADgTIGsmUkPbrxz3e6n5XybLU8iE0mZtnyZJs4C2ZYv2PNEOU+wOQUpjxdGX/irdzcaGTNjQbO2rLiiut7LuNyUy8V466srinsTUfFMY0cMl7HH3TDoZw89lcUV5JwxSEaKuRLG662vKB5Et+1W8yMxZjWp1ZPcsRm7VFOLcdJm66RM/N+AJZmTkvBKv1hv71+md13LIK8KAADgq0Rv5UImbNzz0nm/tZf+56g5qyZcuvbsq9ZPuKZhUsVrhde/MfX6N6df++pZ33h5zJV/HnXpf41Y9cHKJ7b99oLfiU++8s2w72513z1WFWlprXG7G1qtFUNrDY2SrpqYjdEa3PQ/AXeDtRo5ImpDRD8lXSQExdVv7ojbetoriWOejoj9RAnq7nNhKRyb5KX2d4pEJ3XFVJboBEmdtO9FkG0NrdGrAAAAZwa0a2aGoim/eeMXokOSHLLstMl2UXaINqdkz5PsdC7bnILNIch24ZZZVQtm3Pr8h4+JNv7xPY3tQT+9hR44YFU0eEVVdXXlMa2TRdctJJHFu7vVWo/lqy2rqKeJKY0uL15vSVyTqGdltZdWGru1qGopCWPe6pWp3Qam1xZbzWoawpKq5/THZvRSTe6lMZWZdTHeda/2tEgmd9LW3bSpuGRGTJnyouhVAACAMwOyZmZsatlwtLtNlEXRJpFJspNYKdpsot0hOZySwyHbbAKZvj75hjtmLl+1Z2WQ7eRlLsAqLx7YyTC6dnwtE/rEqistPo+ntraysrKMYItprknMV7uI7HTVtKbVudpVsyT2cM96El/7bCXKF9DU1ZzSbWD3gj7VmPE5iXpOf2xGL9XU96RmXb2SPGnxTHql9StSfMwVAAAg92DMo8z45cu3b9i3zjHCaS9w2PMdNrnbbhdlmyhJvCjwDCv4O46Ply+4/7wXPw9+cvtbZZ0dXd1dSqAjXGqfuPr//H8sx7OCk520xKpuMHyeykU9j07Sm9slJSXN9WSDu+9oPR5jGB+SNGOb14xnPqu9/R4QtcMsGV+DudVaSaCnvFlfjOi6jN19L4+K29XnwpI9NulL7e8Up9liib2a5E8a9bfpcrmXro5rsgbIEduPWQsAANG+Ns5aINCumRm7jr3PC3xkEki+FCWeBE1ZFu2yZLdJZzkn/LJ4lcjJ9b67dS7MCXRgTZIwd5+M/KrWAtbCoPhqjWjiqjEfUmwk6uqWGA15fXgqac5Lo0NQj9i7vD3MDi0JLJxhlUjQT6ln35fq9JeaBUmctIiOF9/a0NOriU21Kz8AAEBuQNbMjM+7jrMcSyc6OruRIzmO5zlJ4GRRLJALFhc+NVIY23Ri445Tbxl7adAk82415FfCRm/0dBqYfa+u85rxMbr9y3zsL5YvA49p9qtoRgmZuxYuqUus5+row6VxYq68X76WZjLvJ+OeRsyxSV9qJqV4Uho4G2mvINpXvb5i0WC7xwMAAHx1kDUzhbV+Wmh2pJt0hueEm0csL5QvCKhdzx5eZu6OTpb07yDdBxkSpUorW8UwH9M8TdKsXx/fL8Z4zDAZxoOG0X1h0pCoGrNrjWtmsbXenySOzeSlJm1QJyWR0+zX1E8vLwAAgByGrJkZ4/In6po+zjHxvBElGqVrqqaoqqby3ypYdp44V2e0VUfu+cL/MSlG9qoao6l0oUC0ybxAq0gxbvqMwdQjgzJanUmiQiJ94C/+0cDTdgiyus/Ur4huQSM10ZbQpEQ6VBf3ueNLaqlMsV3OW70opprIhbiXnv7m/+mPzeilJiu5k/o8nrjdCf+3BAAAwBkAWTMzSifMtnOORy5f/YOZvyQpM6wo4ZDKKuJtYx+6yHYFKbD1xF8aj7+gKJo56YqZR/WSUePNGlJk3jTvaSKLhMQK+g4a2g2djgReUmMM0xjhqTT6pbiY3StpkRiRlBMZpae6OFIRram5poZuTUp5nTE4JH3E0KrCfPsNuZ4+rawDc9XUlBhPKlqfiFZBtybTdT6pYzN3qSlI6qQtK6J208unV5+dxx4AAACyC1kzM64+f4EaVgvkkaVj5nxv+o+UoFbAjr1r2qpS52Vk7/HA4Sf23hUIhENBNRRSwyGNTEpIU8P6Nyb23g5WFCXpYQGscOlaeJ3ZTFdU1Wg81kd7khCMu6ZVr7vO2GXqaZw0C8TpvTtLslBDTVRFJOK0Nlal0lHGuBRShdGrhfJ6XS53Da0mxUcgrzOuhTFqoSk5lUqSOjZzl5qC05+06LqlUbvr6e6G1vRGpwIAAPiKYMyjzCBf441PzplxTsnD5c+Q1QMde8Y5JjuEPLIc1AJLtl97qPMjVdGUME2ZQb8a9CuBTjUvLL1W4XaIstFRiD9ZsDgvL08QBJaNe/pzuIkdKSg16RwLAKnBmEcAkFD0mEfImhnTdPDt7/+p/P6K//h2yfetTQyjasp9H9z6fseHuqYrRCgcDoSC/kCwk06/veaJG0u+YxWFXsiaAGeGHMmaf6eu7ji2ndVVOhiIMSaIORpIZOIjG3mG4zTOuUOsto4EgOzA+JpZMXfKZdWX3nvfxmpPywtmgidB84Gmf2s8uLG7o6u7syvQ0e3v7A50+UnKDHUHF154G4ImAED6eCmvYKKLk/Lp717669f4YS73TMYPMuM1v3UYAHwpkDUzye26c/H8pXe+tOh3b//6hP/zn7256LWWP3d3hLpOdnaf7PR3dAfa/YEOf7AjcPOFdyy7psY6DAAA0qJzgi1v/HzBNsaMlla4tCKmRuZ0MlaNCQC+PMiaGUbi5qp/9jS2vHH5H4re3L2hu5P1dyiB9m4SNLtPdZGFs9hxtTc8f8/Vj3IsvnwAgIwgOTLIsrpzQpnoLDRCppk5dV0z5nSZFjMWkDUBvlSIO5k3p/DSl25vevwf/vumGbdMs81wqKPtYUehOL1i6rdrrnvutcqd3zzvRqsoAABkgJEgNT+jBuxnXyyPLqWx0mrCNMMlmbTIApkyzvPgePbi62vbrNWEjDLjKxutVYDhAn2DAABgkHKkb9DX+D9FciTDcDaGL+g4+BLtHmR0EmLojL4UmPYNYmlXob/Z7jGOyyCSIytemF2zfkPVZGtLX0YZxv3YJ3Vl1haqbVPlskfrd2wz11yltyy9f2V5/5UAnBnQNwgAAIaQ3jYTndFCjK7Sm+VG+jTvnRtJlMzMcr2lv2q+xiXsgu/SoFl6i/umW1yljHfHmooFaPuEoQVZEwAAMmzDumff875lrfRjW+NfSTFrJV2R+KjrSmd3+FQHE/2YprVAl+hkJc5c0PrWGqb0/ob1n+jPrKy7e2XjM5+0PnYL2V7/kyW9bxwGONMhawIAQCa98qdVL6995olHHxggbpKg+eTvV5BiG9attjalQenyH1jzxpZbHvrLRT987eIfbrz4XxrLn97+gxcPrnkvdMrfEzGNjJk7QZO64vnWZ9zRd8yLylY23ER+rlmPpk0YMpA1AQAgY0h2fOVPND5qmkbiJsmU5vZoZtAkBcjyy2tXpdm62fU/z266csmeh9Z27N4v2nnHGKdjTJ7olIKHTx16drv3W6sPrNmma5rVtGm1buaI8rKr+r50onjabGsJYGhA1gQAgIwZf04h7Y5jIGmSZMq4uBkdNAlSePw5g+wJQxLkibt/eGr5DzldtY1y2Ec77WPyJj/07xOX/l/bKJt9lJ3MRVk88OTW93/+shoIW02bZvvmYPgal5TdSvuSG9P1Zc9vsnYk1FZfZpRM9eHL1v1WP6E4vsb6yluvj5x9fNmtS2obD1j7Inq7urdtqn2wp/D1lc9v8llFLMmVPFBrfN74j9C4hBZ+cMCPD9ADWRMAADJmdtmVt/90aX9x8z3vW3FBkxQmh5irqTp130+DLz8nj8y3jbTbR5NkabdNGDPxmhvHXV3hGGOXRsm2kTbbSFkusLW/f2THrzcYDZqDDpoknBX/ZI13x2z3Tbe4b5rtYrZ5X/fFBbhebZsqFyzzMoz7sS0xvc5P70ALrfSWBTFH0cxX/JNl9Tu2uejZb3EbvYiqfzK/LGHgO1hftuC71T7GvFQS8Osf+W7xYEpOrbr/fhfD1D9ZH/VJN1X+ZA25woa7r7I2AAwMWRMAADKpv7hJguYTjz6QqaDp/8uL3f/zlFDgFPOd0gi7PNJhG+WQi4vtDodzzFjhnIn2EbJMpgJJGmGT8qUT77bt/2PToONmW/2KFxim9P7W9zbU3b2y7u4Nje990nr71dbeeCRofrfeCppTrW3J8T3/4+odDHNTebm1gfI8ON/Y+Hzre5800rOvrHvmE339826G8b7w3b7tpvWPvMz8fIv+jHWp+nqaF5kXHu07/OfpS052L72JYXYsW/S81Ybqe/5R46OtjL5CgIEgawIAQIYljJsZDJqMopx66Feiwy457UK+Tc6XpXySLO2OaeeyLCuKolQ4RSqwtkt5opgni3nS/qe2hk/5dZ3J1+LvPZ/WoVYvmRcVRT9eWVQ2te/TlkYbJA2arp+nHjQblxQ/so0m2ugmQzPmGu2IMaebfFWdEQ1jGx0NN/2s8btRpzbzIrNt3dt9PncSJcvvNkLtI/9Ju8a31S8iV3jT8yk21sLwhqwJAACZ1zduZixoMoz/jVeYzz7m7RLvkEWnTXRKUr6NTPL4c8wCtvETxHxRyJNEEjTtgugQeJvAqNqRl3cwjD41tNaufWKWTFJhsdngV9nnEclYJGjSNkgSNGMy3Okd8Dx4ffFP6BBIrc+4YzKlEXNdP/9RgnbEyVcvLGWYHS+/Gttg6b4i/u52+RV0KKW+kit5VR0diWnNiuc31S5b5sXdc0gVsiYAAGRFXNw0pR80Cf+br3KSyMsSRyabTbSLokMkiVMoGGEWEEeMEEnQtIs8mRyCIAmCLPCycPx/95K9vB4oVtaYJZM02b36sVtczLb6n8ynvYIejO9qY9i2btmggmbbpspb51e8sM11Ex0CKa6t1HfwQzIvmZKwwqkzErWsZp4xEpP3ke+ST4e755AyZE0AAMiWuLiZkaBJhHe/zwo8Kwq8yPOSwEm02VKwi5zNYRbg7XaSLDkb2cvxIinAcSLHC1xHy6dWAT1gLiStqGxl43tbGsxeQS98t/ji6/u0cc4uKaLDFXlfN243J6mN9s6p3zG75rEtjXF3yXMJRmKCwUPWBACALOqJm5kKmoT2xacsz0Umnr7tnCfRk2dZ1SxAFjiR5wSOvgadZ+hE347OqIGw6g8b3YMG1SF9arnZK8hq4/xxXG+bmd/b0Prz2cyONRW39nmGMiEaNJd5S29pWL+hasCHO5sPJrx3b3Zav2BGtt+fbj2meYsb7zSCQUDWBACA7DLjZqaCJkViYzyd/l+w01qhC4kDJcsOMmdGKSpbuZpkSmbb7kPWlh5F391AX/yzY1nx6ePmAevxx2dWRr86KE7RZTe6errmxGnbuG4Hw5QWF1vrWWKO33RLw90rzQc3KzCyJqQEWRMAALKOpMyMBU2G4cdO1FWNTgqZq5qqaYqqK4rW/oVZQDn5mRrWtLCua5qm6prK0DcHaYyYZ+NkgZZILW76Gjd5Ypsw+xtxnSi/e0tScdNMirHDGyVg9Q3v01baM4Tn7fGPeGaW50Fz/CbjMc2yH9WUMkyigZYA+oWsCQAAZxhx1sU6CZdhRVVUNaRoQUUJKEp3KHz8CNmrEJ8eobfLg4oaVMmkhVQtrJHC+ReMNWtI1cFHKxaMZ2+9vvLBJWQqu3h8xQt0wMt+hv6ZSuImzWQDx01zHKUXvlt26/WJpt5b1b21Ga8Lotdw63jWGMLTGFnJKpYVjUvoJy29f4l1lsjo7riTDslD1gQAgDOM/doFaohEyZBGpkAgHAgr/lC4KxQ+sj8cDnd9cZz57JjSRTaGVb8RQ0OqYuTOcVf23m5WFcV8aWUSii77mbt0NrNjW/0La8jkLZ3tfmxLzCiY8aZWPZNE3DR4d2xLNFl7DbQ2+pCo8bogeg07Zrtuur9h/ScpjqyUKvMVQbNr7o9qOu1pZ8WddEgSm/T/pwEAAMTYfsxa+LLp+vEb5rHH9ksj88QRzvwCRXRKokPiZUGbMVdtP8627dZCmhpUwt3hcEco2BEOngpoHHfZi/8qOmTW6Ez0/4KLHY48XhBY+ggnAGTS18ZZCwSyJgAADNJXljUZJvju5i9uqxDpOyod9nze5tAFu8RLHMfzfu4s+gRnKKwFQ+GugNIZUE52BNu7Clb8wXHjd6zjASCborMm7qEDAMCZR55zad7i5Up7V7ijO9CudJ1kAie6Ayf8oZGT9XFTwifaQyc7Qyc6wye7SNAMdfptN30fQRPgK4F2TQAAGKSvsF3T1FH3cOd/PCA4ZF6WBLsgjc772oYmQZK2LCwP79+nBUNqIKx0+e0Lby/49Uo29g1GAJA9aNcEAIChIL/yztFPv6qPnRo62R74orPz8InDr7zQtvEv/o/2hr5oD33RoTrGjHj42RFLH0HQBPiqoF0TAAAG6Stv17ToenDzG90bXwo3v6cd/4TRNW7MWHHm39n/z/W2q65nBGNATQD4EqFvEAAAZECuZE0AyDG4hw4AAAAAXwZkTQAAAADIFmRNAAAAAMgWZE0AAAAAyBZkTQAAAADIFmRNAAAAAMgWZE0AAAAAyBaMrwkAAAAA2YJ2TQAAAADIFmRNAAAAAMgWZE0AAAAAyBZkTQAAAADIFmRNAAAAAMgW9EPPMPXj1R3HtrO6ynIsS/9wBJlFJj6ykWfIKu9kz6m2jgQAAAAYctCumWG8lFcw0cVJ+TTE0xxv/DCXeybjB52pfuswAAAAgKEIWTPjdE6w5Y2fL9jGmNHSCpdWxNTInE7GqjEBAAAADFnImhlHcmSQZXXnhDLRWWiETDNz6rpmzOkyLWYsIGsCAADAUIasmXFGgtT8jBqwn32xPLqUxkqrCdMMl2TSIgtkGgRPJcuyZbU+azWnGNfGVnqs1dyUy18gAADAkIKsmXGRBKl1MeopKX+a2X4ZPydljBXjEAAAAIChCVkz03rTo85oIUZXjWxJw6WVLOmykTONFWMOAAAAMDQha2ZcJD7qekgNBsIBI2QacZPssRboEp2sxAkAAAAwNCFrZlhACa778K3bGx6eu+bHJU/fXvrUty/f+PQd3vXP+f7WTnJnJGIaGRNBEwAAAIY4ZM1MeqV57VVr71y+ddWWEx8GbWHHSNkx0qY6tZ3BYzU+77Ubn1nTut1s5KQtmlbrJgAAAMCQhayZGZquLW9YvKThtg6+214gOQpkR75kp5Nsd4r2PMGeL6h27fd7Nld5Xw6qYatp02zfTIPPU1tZRjt+U2VllZ6EXatjSiUq1tt5PLbCytp+KvR5KstiakzQp/t019bbGZzWZhajp4wUi9oavTmKcYaoy4i/XF8t3Wl+LKuufjvIm2VjC8R/ytrc7l0PAACQg5A1M+PBjXeu2/20nG+z5UlkIinTli/TxFkg2/IFe54o5wk2pyDl8cLoC3/17kYjY6YbNFtry4orqusZN+VyMV5vfUVxn5F8SF6ipbyMyyzXTzGqxaiw2aqQYbz11RXFfdIZrbC4ot4bU+O6V2PrM6uKXBupqt+TGp/CPKd5ymKa94yrjjqcbI47moTDmM9FLiPh5RKeSnq91koiJFQWV5MC7obWunJrG4nCxqfsrd67riXB9QMAAMBA6A1dSM/GPS+d91t76X+OmrNqwqVrz75q/YRrGiZVvFZ4/RtTr39z+rWvnvWNl8dc+edRl/7XiFUfrHxi228v+J345CvfDPvuVvfdY1WRmga39bdHspG1iWqtIamMYVw1vRsTbIpsjN4WqdAVUzDRwZFtcWduqIms91xbosPIBVsbiJ6TRhWMlCMSbI4+mmxzx16EVSrqOGsDyYkxF2OdumebdSExtfWpi2htaI2pBQAAAE4L7ZrpUjTlN2/8QnRIkkOWnTbZLsoO0eaU7HmSnc5lm1OwOQTZLtwyq2rBjFuf//Ax0cY/vqexPeint9ADB6yKUuduqCsvspapoqrVNCF5q1dG2vY8K83Wusaq2HJLSbqKKhbhXhpT0CzHeKPaLHsqjDtzeVXMep9zWpfGNPdpGXTVrI4qaJ2yz+brFtKj69dHXXFRVV3sRVilvLtbrXWL11sS9wVE89WWVdQbqTKmttbdtB20ZEb0YUXlRf3VAgAAAIkha6ZrU8uGo91toiyKNolMkp3EStFmE+0OyeGUHA7ZZhPI9PXJN9wxc/mqPSuDbCcvcwFWefHATobRteNrmdAnVl2pcS+I3O7tYWWySKLzrDdS1JI+5coX0EQXH/z6VmiW62VUmOjE8foWKZpRYi3Fio1zDFM8k34C18LrYjb3dzTj83hqaysrK8sI1rgP3keiL8BC78OTY0jQjA+j5mXUr+jnCVgAAABIDrJmut5s2cCJPC8KgiiIkihKgkwmG42bDrtEJpssFI0uvXPm48cCba8ffU4UOUHkeJH96xEjxWgB/fgao6YUuWYWW0tRYjKZr6WZzL3VxVbnlii0KS91ZoVJRM3kJfwUCZjZL5rZ3ae4oqK6ur6eXleJ8WRn8rzrFvUTNImiqtUN9EnU+gry7fXb6QoAAABOA1kzXbuOvc8LfGQigZMXJV6SeFkW7bJkt0lnOSf8sniVyMn1vrt1LswJHMexHM/uPnnMqkILWAvZYXZuSWDhDKvEGchXu4h293FZz4k2EnV1S4xW3XjxLacRrpISWty7rs+zBIai8rpGvZUETqs3FYvACQAAkDpkzXR93nWc5Vg68SREGjmS43iekwROFsUCuWBx4VMjhbFNJzbuOPWWsZcGTTLvVkN+JWz0Rk+3Q3ovs+XRSldmI6dr4ZK6xOKfsUxS32cuv3y+V9dZD6L2fcgyaTOXNNIuQCRIJuwgT9HA2Uh7BZltnIv6KwcAAACJIWumj7V+Wmh2pJt0hueEm0csL5QvCKhdzx5eZu6OTpb02x90zozushNhdt3puS9t3HhOVG6QrM43fTsVfekSpkozaKekqKqR9kL39hlRKRaJnGa3q/h+RwAAADAwZM10jcufqGv6OMfE80aUaJSuqZqiqprKf6tg2XniXJ3RVh255wv/x6QY2atqjKbShQLRJvMCrWKQcdNbvSjmrq7PU2k8huleGnn8MNLhvLjP3V9SNuHo6KdhdRPv0xDo89R+uS1+VtedqH7p5BMtStg16HTK61r7xk2fxxP3laXYZgoAAAAGZM10lU6Ybeccj1y++gczf0lSZlhRwiGVVcTbxj50ke0KUmDrib80Hn9BUTRz0hUzj+olo8abNQySy+0moa+Ydlyh/bBpN5nI2D1WCYIEKdoeF+nhYpSkb8IhZVNuAzRYFRodjiIV0urWWfu/JJFBkCp6Pj75RCU1xrhKKSvq/VA9cbNlRdRXRj8i/XLpIFPmbgAAAEgOsma6rj5/gRpWC+SRpWPmfG/6j5SgVsCOvWvaqlLnZWTv8cDhJ/beFQiEQ0E1FFLDIY1MSkhTw/o3Jvb2wFYURU/5fZX0ccMGkpG89QS9de6uaejbpbqoipayergYJb2uxCWTRCvs6TJjVEjqa1gdO0ZR1hkfy3ijkHERjLumVa+7ztqZOlpddNwsum5p1Cc0P2JrTIoHAACAZLCpRxyIQb7AG5+cM+OckofLnyGrBzr2jHNMdgh5ZDmoBZZsv/ZQ50eqoilhmjKDfjXoVwKdal5Yeq3C7RBlo6MQf7JgcV5eniAILBv39CcAAADAGQxZMwOaDr79/T+V31/xH98u+b61iWFUTbnvg1vf7/hQ13SFCIXDgVDQHwh20um31zxxY8l3rKIAAAAAQxTuoWfA3CmXVV96730bqz0tL5jZnQTNB5r+rfHgxu6Oru7OrkBHt7+zO9DlJykz1B1ceOFtCJoAAAAwHPD33nuvtQhpuHiyS9CFB16v7lQ7Lhz797/6f5X/e/DlcEgNBZlwIBQOhkPdoZA/GOoKfmeW+56rH8W9cgAAABgOcA89k949tPmBjT890OHTOI0XeZYJM6ykM7qmaFpYHW+fdNdVD33zvBut0gAAAABDHbJmhpFk2bjvzY0frd9xdNvxrqO6rp3lGDdz3EXfmHHDlcXXCZwxoCYAAADA8ICsCQAAAADZgr5BAAAAAJAtyJoAAAAAkC24hw4AAMOa+vHqjmPbWV1lOZalfziCzCITH9nIM2SVd7LnVFtHAkAS0K4JAADDGi/lFUx0cVI+bXyh7S/GD3O5ZzJ+0Jnqtw4DgOQgawIAwDCnc4Itb/x8wTbGjJZWuLQipkbmdDJWjQkAUoCsCQAAuau5ufnQoUPWSraQHBlkWd05oUx0Fhoh08ycuq4Zc7pMixkLyJoAqUHWBACAHEWC5s6dO71e78GDB61NWWEkSM3PqAH72RfLo0tprLSaMM1wSSYtskCmLPFUskSlx1rNBuMUZbU+axXgy4CsCQAAuWjXrl0kaJIFXdffeeedAwcOmNuzIJIgtS5GPSXlTzPbL+PnpIyxYhwCAMlC1gQAgFyUn5/Psqy5TDLeli1b9u/fb65mWG961BktxOiqkS1puLSSJV02cqaxYswBIFnImgAAkIsKCwtdLld03Ny6deu+ffvM1YyKxEddD6nBQDhghEwjbpI91gJdopOVOAEgWciaAACQo0jcLCsr4zjrnyqS+ZqamjIeNwNKcN2Hb93e8PDcNT8uefr20qe+ffnGp+/wrn/O97d2kjsjEdPImAiaAClD1gQAgNw1efLkuLi5devWvXv3mqvpe6V57VVr71y+ddWWEx8GbWHHSNkx0qY6tZ3BYzU+77Ubn1nTut1s5KQtmlbrJgCkAFkTAABy2qRJk6LjJtHU1OTzpduXWtO15Q2LlzTc1sF32wskR4HsyJfsdJLtTtGeJ9jzBdWu/X7P5irvy0E1bDVtmu2bg+Hz1FaWldGu5oayylpPUp/BOM46iC0rqyRH+Wrphr491mOKWmX7kaDWaL1n8Hmscj2nG+wHgeEKWRMAAHIdiZuXXnppdNx8991304ybD268c93up+V8my1PIhNJmbZ8mSbOAtmWL9jzRDlPsDkFKY8XRl/4q3c3Ghlz8EGztqy4orrey7jclMvFeOurK4pPO8BR73HmgeSwiuKylbut3dFIJow5BeM1yiYa4KjVrJUxCw5U0lNZXEGq7DXYDwLDGLImAACckaxHKAfl9Y9e/uPf/iA5ZNlhs9FWTIlMDqfkyJPp5BBkp2Anc4fw00sf+PvJ8zYd/2jNR9uMqDnYk5a4G1p1vbGxjmps1FtrXAxTv2LAsS5J0qsmQY8eGTlQJwcy9THxjyIRkGZCVw09R29JkgSrF8WfwltdUV1iXIxZ0LqWBCWbV6yoN6qk6srNjYP5IDCsIWsCAECuO3z48ObNmzVNs9YZZs6cOcXFxdZKihRN+c0bvxAdEs2aTptsF2WHaHPSuGmnc9nmFGwkaNqFW2ZVLZhx6/MfPiba+Mf3NLYH/TTfBgYx0mdRVV1deZG1Yii6biENeLtbrfW+fLUr6skPd0PskUVVq2m6i+FZaWbSxqrYkkvdNEOujG907KfKPiW93pK4Kgf1QWCYQ9YEAICcRoJmY2NjXNAsKorJOynZ1LLhaHebKIuiTSKTZCexUrTZRLvDaNp0yDabQKavT77hjpnLV+1ZGWQ7eZkLsMqLB3YyjK4dX8uEPrHqSo3P46mtraysLCNYo8lyIK27SQFXzRKrPbGXme6ieNaTUJqoZPkCEjaZ5pbYNkf3gv6qjCuZqEpDah8EhjlkTQAAyF1tbW3RQZNl2blz56YTNIk3WzZwIs+LgiAKoiSKkiCTyUbjpsMukckmC0WjS++c+fixQNvrR58TRU4QOV5k/3rESGJaQD++xqgpeWYPm+KKiurq+vpmsqGEPlE5IF+LUW7G6T+rWdJbXWx21olWQVtG47hmJmgQLppRYi0NbBAfBIY7ZE0AAMhRJGh6vd64oHnuueeaq4O269j7vMBHJhI4eVHiJYmXZdEuS3abdJZzwi+LV4mcXO+7W+fCnMBxHMvx7O6Tx6wqtIC1kBxf7SLzaUr6pCN91pE+7bgkrnEybWZvnQQWzrBKpKpP0P1yPggMMciaAACQiw4dOhTXojlv3rzp06ebq+n4vOs4y7F04kmINHIkx/E8JwmcLIoFcsHiwqdGCmObTmzcceotYy8NmmTerYb8StjoHpRSDyHfq+uspymjn3Q0bpGfVvz9b0PcoWabpGvhEtpbJ4GY0/YjqVbUdD4IDF/ImgAAkIva29v1SE9zEjTnz58/bdo0czVt1nsvI2h2pJt0hueEm0csL5QvCKhdzx5eZu6OTpb0X82UciaVMIyZ4W4AZoL0rnu1T9g0Hs+MVjzTlbhkQgmrNDoXJby7HmVwHwSGO2RNAADIRSUGsmAGzalTp5rb0zcuf6Ku6eMcE88bUaJRuqZqiqpqKv+tgmXniXN1Rlt15J4v/B+TYmSvqjGaShcKRJvMC7SK1OKmEQWZ+vW9nbx9nspFp+1RU74k0VBE5NA+D2FGOpwX9xm8nZSu7DMaEakypmCkSvfS2D7nfQzyg8Awh6wJAAA5atasWSRuXnLJJRkMmkTphNl2zvHI5at/MPOXJGWGFSUcUllFvG3sQxfZriAFtp74S+PxFxRFMyddMfOoXjJqvFlDiqxO3vUV9AU9tPc2W1xRX1LTZ+SieD1DERUbr/YxjiSHNtc09Dm0vM4c5rKip6j5ah9yoj6tji632ygYVaXRjb01MoJmvwb7QWB4Q9YEAIDcReLmlClTrJUMufr8BWpYLZBHlo6Z873pP1KCWgE79q5pq0qdl5G9xwOHn9h7VyAQDgXVUEgNhzQyKSFNDevfmNh7i1lRlJ5b/KdVVNXY2kC7a3u99QTjrmnV666zdg7EOLAm6kiXu6G1sSrRne5IUeMtQJTX63K5a2jx+MbKmUtoUfrGH1qMhs+ExRIY9AeB4YxN/v9VAAAAhgDyD9+NT86ZcU7Jw+XPkNUDHXvGOSY7hDyyHNQCS7Zfe6jzI1XRlDBNmUG/GvQrgU41Lyy9VuF2iLLRUYg/WbA4Ly9PEASWjXv680vgqaSDGbkbet7lA5DDkDUzRv14dcex7ayu0r6NRhdHs3NjZOIjG3mGrPJO9pxq60gAAPhyNR18+/t/Kr+/4j++XfJ9axP5Na4p931w6/sdH+qarhChcDgQCvoDwU46/faaJ24s+Y5V9KtF30le7XXVJNcWCfAVwz30jOGlvIKJLk7Kp/GdJnjjh7ncMxk/6Ez1W4cBAMCXbu6Uy6ovvfe+jdWelhfMNhcSNB9o+rfGgxu7O7q6O7sCHd3+zu5Al5+kzFB3cOGFt+VK0CRR0xh5KKlR3gFyAH/vvfdai5Cm7t0sL0t5k9TQKY1ESXpbhfxhaR9KjiwaK3Td+smOoA8GAQDAV+LiyS5BFx54vbpT7bhw7N//6v9V/u/Bl8MhNRRkwoFQOBgOdYdC/mCoK/idWe57rn6U/Nq2jvzyeGorG05cML94tLVO+DyVxbesp1151v5mftR2gJw1XO6hNzc3FxQUFBYWWuvZ8Nl/02EwWInhHf5PP1C626zb6CRp0pvm5qjBdDhgxlhnp9xtHQgAAF+Rdw9tfmDjTw90+DRO40WeZcLk17jO6JqiaWF1vH3SXVc99M3zbrRKf9nM5zJJsHS56fBPzc31XmN8IZe7oRGPasKZYlhkTRI0d+7cSf4n6SWXXJLx/oy9Pltn3CXXGM7J8Hmhjn2hEztp2jSzJp3oGypI4mToD5I1l1oHZoD58A6eEwcASBlJlo373tz40fodR7cd7zqq69pZjnEzx130jRk3XFl8ncAZA2p+RXye2pUr1jV7zYhJkNS5cOmSZF4EBJArhn7W3LVr144dO8xlEjczOyBwjM/+ZD6RSZc5G8MXdBx8yWzXJGi+NBo1ad8gmj55bso9xmEZgawJAAAAuWjo9w3Kz8/veciGBOstW7bs37/fXM2w3tCuM1qI0VVyOiPKkz/GPiPWG1voT2MOAAAAMJQN/axZWFjocrmi4+bWrVv37dtnrmZUJD7qutLZHT7VwWi0yzkNl2SPtUCX6GQlTgAAAIChbFiMeUTiZllZGb2RbSCZr6mpKeNxU+nyH1jzxpZbHvrLRT987eIfbrz4XxrLn97+gxcPrnkvdMrfEzGNjImgCQAAAMPCcBlfc/LkyXFxc+vWrXv37jVX09f23LObrlyy56G1Hbv3i3beMcbpGJMnOqXg4VOHnt3u/dbqA2u26ZpmNW1arZsAAAAAQ9xwyZrEpEmTouMm0dTU5PP5rJXBIgnyg8U/3PnTH3K6ahvlsI922sfkTX7o3ycu/b+2UTb7KDuZi7J44Mmt7//8ZTUQtpo2zfbNwfF5aivL2IiySk//n8EoGlW2rLK2n9I+nye+ZFTB+J21HmsHAAAAwACG3Tsqjxw5snnzZk3TrHWGmTNnTlHR4EeP2PmzqiN/fEYqcEpOVnSIvCxwBQXnP7851HFy3/cuVUKqFlQVvxLuDIU6QyMvnvS133+LFeiYR9y0+6wqUmL2OCcLkeHW6uu9jMvtZsjP2H7oPSXpblLSLGqsxg/MRpJkRWQfLWpWGnn9Wc8Ab+Y+Or4bXo0GAAAAyRhG7Zr9SSdtH33pxbY1T4kFTplkzRF2eaTDNsohFxfbHQ7nmLHCORPtI2SZTAWSNMIm5Usn3m3b/8cm4/754E7qqTTio7uhVW9srKMadb21hgZNq0REVEndLGkUbXAzjLe+ojKqXZJkUho0SQLtKWqUXDjD2r2CBE0SLiP76M6l1xn7AAAAAAY0vLLm4cOH+zZqFhcXWysp0hVl99JfSQ67nGcXC2xyvizlk2Rpd0w7l2VZURSlwilSZLuUJ4p5spgn7X9qa/iUn+bbwAGroqSZsY/kx7qYYXyLqlbXuKxlSz8lmaLyulZatn5Fzx1yz0ozkzbGVVpujRXcupvG2Nj37haVp9ESDAAAAMPHMMqaJGg2NjZm8O75J6++Ej7+seCQBIcsOW2iU5LybWSSx59jFrCNnyDmi0KeJJKgaRdEh8DbBEbVjry8gz7neXwtE/rELJkkI/a5apb0Ga+96LqFsWGz35KRst51r5ph07PeCKUL+h0Dvngmrbp+xUCPhQIAAAAkNFyyZltbW3TQZFl27ty56QRN4pOGVzlJ5GWJt0m83SbaRdEhksQpFIwwC4gjRogkaNpFnkwOQZAEQRZ4WTj+v0b/dy2gH19jFEySr6WZzGNbGBMbsGTRDPrwpsUsOUDUpK2mDW4XvfFebHQZQuQEAACApA2LrEmCptfrjQua5557rrk6aO0fvM8JPCcKvMjzksBJtNlSsIuczWEW4O122lXIRvZyvEgKcJzI8QLX0fKpWYDETWshpxWV1zXSJz1dJHEakROBEwAAAJIz9LPmoUOH4lo0582bN336dHM1HcHPPmV52qGcoxNP33bO86zIs6xqFiALnMhzAkdfg84zdKJvR2fUQFj1h43uQYPoIdTckiDnmQ9VxklYMmGjZz8lo9HAaXYuMto4F0WPiAQAAACQ2NDPmu3t7T09zUnQnD9//rRp08zVNJEEaS310un/BTutFbqQOFDSV2amnDPNm989T1pGMR+67GU9k1m9MsEwmL5X19FnOWeaPaIGKpkIiZxGRyTv7lZrCwAAAEC/hn7WLDGQBTNoTp061dyePtv4ibqqkUlTNE1VNbqg6oqitX9hFlBOfqaGNS2s6xopoGsqQ98cpDFino2TBVoixbhZvsSIedWxjYo+T6Ux/GW0oqqlbvKjvqIstv2RlDWHQloaGR2z/5K1xgafxxN3wzxhGyoAAABAAsPiec1Zs2aRuHnJJZdkMGgSI752MQmXWlhRFVUNKVpQUQKK0h0KHz9C9irEp0fo7fKgogZVMmkhUlgjhfMvGGvWkDJrdCNvtdlNhyhj2eKK5pqG+EGPGGtwo6iiZlmSSl01rdFDufdXcp25u2WF1SkosodWQQdTMncDAAAA9G9YZE2CxM0pU6ZYKxky4R8WqCESJUNqIKT6A+FAWPGHwl2h8JH94XC464vjzGfHlC6yMaz6jRgaUhUjd467sndET5JIUxpMvqiqsbWhxk2zobeeMIZgb6xKNEQoLdrTo4fyulzuGjoKfPwbfxKWbFh9HS1WdN3SqB3mntaYrAoAAADQn2H3jspM0vX/LZsXOrJfHpUnj3TKTkV0SqJD4mVBmzFXbT/Otu3WQpoaVMLd4XBHKNgRDp4KaBx32Yv/Kjpk1uhMdLJgcV5eniAILH2EEwAAAGBIQdZMy+fezU3/WCHRd1Q6pDxesuuCXeIl2i2dkc7SVI02fAZCoa5AuCMQOtkRONU167E/nLPwO9bxAAAAAEPacLmHniVjXJcW/2p5uL0r2N4d6lAC7UzgRHfghD80cnL47CnBL9oDJzrJFDrRRYJmsMN/zv/3fQRNAAAAGD7QrpkBvt8/3PrbB0SHLNgkwS5wo/K+9kqTIEnbbi5X9u9TgyHFHw53+Scvun3mb1YmGikJAAC+MurHqzuObWd1leVYlv7hCDKLTHxkozFIMu9kz6m2jgSAJCD3ZEDRT++c++Kr0sSpgRPt/s87/YdPHH7lhcOv/6X7o72Bz9sDn3dw+WMueuLZCx96hPyyso4BAIDcwEt5BRNdnJRPG19o+4vxw1zumYwfdKb6rcMAIDlo18wcXf900xtHX3np1PvvBT/5RNc0eezYgtK/G19x/bjy61nBGFATAAByzWf/w7Ai+cew+9PtavAL2oLJGi2a5A/PMXSVN1Yj88JfWwcCQBKQNQEAIHc1NzcXFBQUFhZa69nw2X/TV2uwEsM7/J9+oHS3WbfRzcTJ03xJUidZpu+LI1lzyt3WgQCQBNzSBQCAHEWC5s6dO71e78GDB61NWWHcHNf8jBqwn32xPLrUumNuTrRFhkxaZIFMAJACZE0AAMhFu3btIkGTLOi6/s477xw4cMDcngWRBKl1MeopKX+a+bBm/JyUMVaMQwAgWciaAACQi/Lz83teckEy3pYtW/bv32+uZlhvetQZLcToqpEtabi0kiVdNnKmsWLMASBZyJoAAJCLCgsLXS5XdNzcunXrvn37zNWMisRHXQ+pwUA4YIRMI26SPdYCXaKTlTgBIFnoGwQAALmrra3N6/Vqmmaukug5d+7c6dOnm6sZETi6+uXWdzbuf3fHZ/tOBrsYlnFywvkFZ10xfvqCaReOkO3W4Jo8b3QY4rmpy60jASAJyJoAAJDTDh8+3NjY2BM3CRI3zz33XGslPa80r/3N64tPKB28yJGJNqOyrKaGNEVXw7pNF354/vxbZlxMu5+bWZMjWfNe62AASALuoQMAQE6bNGlSWVkZyXnWOsM0NTX5fD5rZbA0XVvesHhJw20dfLe9QHIUyI58yU4n2e4U7XmCPV9Q7drv92yu8r4cVMP0Zjo5LK0GGp/PU1lWRgKtqayssjb2Y/g8tfEFPPEf1FNJ99ADaW1mQTaqpqit0Zsj0jzclPR1spUeq7RRkEhUFIa2M75dc/sxawEAhp6vjbMWAI4cObJ58+bo1s05c+YUFRVZK6l74LWfPb+zTnLKkqyLMi9KPCfQhkuG5ZRQl67oYYW2b4YCatnEcvZU66OuGzjeGG9z2n1WFakhGa6i3kuXXG53CcM015M1V01rY5X5IXy1ZcXVUfvNAsZqQ2NdOV0ykAxXUe+qaVi4rqKa7Csp6SnobtAXrKcniToBObz3FFSah6d4nYy7pqa5utrrMs4UfaqokjDEIWsCQO5C1oQefbPm7Nmzi4uLrZUUvf7Ry4vX3yzn22SnTbYpkk0gWZPeJOfpgO1KoF1RdTWshUPa7TOXhsPqo5vu+fkFVyy6YC5Jo9y0+61aUmAFNBLHVteV9yQ3n6e2tbjKWDeTGQlhrVH7aSNjsbm5N5tZJWNCYG/+S7Q5Jtll5vBUrtNVs7o3rUa+iPgEC0MY7qEDAECuO3z4cN9GzUEHTUVTfvPGL0SHJDlkmjXtouwQbU7JnifZ6Vy2OQWbQ5Dtwi2zqhbMuPX5Dx8Tbfzjexrbg37aPhNIfaRPz0oa5Yx2v+iAVVRuBk2SwFbQWOZuiN1PCtS11rgYpn5F/M1sEuCislpR1VK3sRC3+bqF5GCmfr3HXO81yMNTv07GvTQmVFqn8q57FTfShw1kTQAAyGl9+walefd8U8uGo91toiyKNolMkp3EStFmE+0OyeGUHA7ZZhPI9PXJN9wxc/mqPSuDbCcvcwFWefHATobRteNrmdAnVl3J8aw3EtqCfm8bt+4mUdRVsyRBATPw9clmJTNiv4DimTQWuhZeF5sBZ9Cb3AkM8vDUr7Pvhy5fYOZaGDaQNQEAIHe1tbVFB03WGPMonaBJvNmygRN5XhQEURAlUZQEmUw2GjcddolMNlkoGl1658zHjwXaXj/6nChyAu2lzv71iJGjtIB+fI1RU5J8Lc1kPkDUNAvE5z9Lwrzomplco66ZIfsY5OGpXycAsiYAAOSshINrpj/a0a5j7/MCH5lI4KQdgySJl2XRLkt2m3SWc8Ivi1eJnFzvu1vnwmafIY5nd5+MdBHQAtYCAJwOsiYAAOSiQ4cOxbVozps3LyOjuH/edZzlWDrRETONHMlxPM9JAieLYoFcsLjwqZHC2KYTG3ecesvYS4MmmXerIb8Stl4glLLmltM8odhPgQEbE78KZ8p1Qq5A1gQAgFzU3t7eM1IKCZrz58+fNm2auZo2672XETQ70k06w3PCzSOWF8oXBNSuZw8vM3dHJ0v6r2bKOdN6lLF6ZZ8uOpaBCvheXUefkUzypnd2nSnXCbkFWRMAAHJRiYEsmEFz6tSp5vb0jcufqGv6OMfE80aUaJSuqZqiqprKf6tg2XniXJ3RVh255wv/x6QY2atqjKbShQLRJvMCrSLFuGl1vq6vMMZQ7+Xz1Job+i9QaQxG5F4a3Zf7q3OmXCfkFGRNAADIUbNmzSJx85JLLslg0CRKJ8y2c45HLl/9g5m/JCkzrCjhkMoq4m1jH7rIdgUpsPXEXxqPv6AomjnpiplH9ZJR480aUmeNCeStLjbeskOVsWxxxTprf78F6mm/79bcGfj8TLlOyCHImgAAkLtI3JwyZYq1kiFXn79ADasF8sjSMXO+N/1HSlArYMfeNW1VqfMysvd44PATe+8KBMKhoBoKqeEQHdFdCWlqWP/GxN4bxIqipPYylKKqxtbWBrfLxXi99RR9l07D6t5BhhIWqGlo1XNs0PMz5TohZ+C9QQCQu/DeIMgG8g/fjU/OmXFOycPlz5DVAx17xjkmO4Q8shzUAku2X3uo8yNV0RTjvUFBvxr0K4FONS8svVbhdoiy0VGIP1mwOC8vTxAElo17+hMAYiBrZszfqas7jm1ndZX2bTS6OJqdGyMTH9nIMxyncc4dYrV1JAD0A1kTsqTp4Nvf/1P5/RX/8e2S71ubGEbVlPs+uPX9jg91TVeIUDgcCAX9gWAnnX57zRM3lnzHKgoAScM99IzhpbyCiS5OyqfxnSZ444e53DMZP8iM1/zWYQAA8KWbO+Wy6kvvvW9jtaflBbPNhQTNB5r+rfHgxu6Oru7OrkBHt7+zO9DlJykz1B1ceOFtCJoAg8Pfe++91uKZ6WiXtfCVm8DtZnlZypukhk5pqp/2nKR/WNqHkiOLxgpdt35+ItAHgwBgABPoXU2ArLh4skvQhQder+5UOy4c+/e/+n+V/3vw5XBIDQWZcCAUDoZD3aGQPxjqCn5nlvueqx8lv7etIwEgFcPlHvrePc3O/ILx5xRa61nwNf6/6TAYrMTwDv+nHyjdbdZtdJI0yZw3Rw2mwwEzxvr7trutIwGgH7iHDtn27qHND2z86YEOn8ZpvMizTJj8GtcZXVM0LayOt0+666qHvnnejVZpAEjdsLiHToJm64c7P3jXe/TwQWtTVhh3yTU/owbsZ18sjy617pibE830ZNIiC2TKOM+D49mLr69ts1YTMsqMr2y0VgEAhrU5hZe+dHvT4//w3zfNuGWabYZDHW0POwrF6RVTv11z3XOvVe5E0ARI09DPmns/2kWCJlnQdX3Htnc+bjtgbs+CSILUuhj1lJQ/zXxYM35OyhgrxiG5om1T5a3XkwxqTmW3LvEMGFgBAIYOlmEvnf6N+8v/Y/1tW7yLD77zk7ZXbt/20A1PfvO8fxA4Y+R2AEjD0M+azrz8nodsSMbb+d6WI4f2m6sZ1psedUYLMbpqZEsaLq1kSZeNnGmsGPNc4Gtcwi74bv2ObUzpLe6bbnGVMt4dayoWoO0TAAAA0jb0s+b4cwr/bo4rOm42b9965OA+czWjIvFR15XO7vCpDkbradE0TkwX6BKdrMSZC1rfWsOU3t+w/hP9mZV1d69sfOaT1sduIdvrf7Kkv1f3AgAAACRlWDyvSeLmRXPLOM76sCTzNb/flPG4qXT5D6x5Y8stD/3loh++dvEPN178L43lT2//wYsH17wXOuXviZhGxsydoEld8XzrM+7yydYaUVS2suEm8nPNejRtAgAAQDqGRdYkxk2c/HexcXPn9q2HD+w1V9PX9T/PbrpyyZ6H1nbs3i/aeccYp2NMnuiUgodPHXp2u/dbqw+s2aZrmtW0abVu5ojysqv6vlaseNpsawkAAABg0IZL1iTGTZgUHTeJ5veb2vb7rJXBIgnyxN0/PLX8h5yu2kY57KOd9jF5kx/694lL/69tlM0+yk7moiweeHLr+z9/WQ2EraZNs31zMHyNS8putTrxsBdfX/b8JmtHQm31ZUbJVB++bN2/zVqK5Wusj+tFVNsY39uqt6t726baB3sKX1/5/Ka4bzu5kgdqjc8b/xEal9DCDw748QEAAOCrNYyyJkHi5kVzL42Om7v+9m6acfPUfT8NvvycPDLfNtJuH02Spd02YczEa24cd3WFY4xdGiXbRtpsI2W5wNb+/pEdv95gNGgOOmiScFb8kzXeHbPdN93ivmm2i9nmfd3X7/W3bapcsMzLMO7HttSVWduSc6CFVnrLgpijaOYr/smy+h3bXPTst7iNXkTVP5lfljDwHawvW/Ddah9jXirDbKt/5LvFgyk5ter++10MU/9kfdQn3VT5kzXkChvuvsraAAAAADloeGXNhKxHKAfF/5cXu//nKaHAKeY7pRF2eaTDNsohFxfbHQ7nmLHCORPtI2SZTAWSNMIm5UsnSLL9Y9Og42Zb/YoXGKb0/tb3NtTdvbLu7g2N733SevvV1t54JGh+t94KmlOtbcnxPf/j6h0Mc1N5ubWB8jw439j4fOt7nzTSs6+se+YTff3zbobxvvDdvu2m9Y+8zPx8i/6Mdan6epoXmRce7Tv85+lLTnYvvYlhdixb9LzVhup7/lHjo62MvkIAAADIOcMrax47evhvTZs1TbPWGebCi+YUTi+2VlKlKKce+pXosEtOu5Bvk/NlKZ8kS7tj2rksy4qiKBVOkQqs7VKeKObJYp60/6mt4VN+km/ztZRH+jzU6iXzoqLoxyuLyqb2fdrSaIOkQdP189SDZuOS4ke20UQb3WRoxlyjHTHmdJOvqjOiYWyjo+GmnzV+N+rUZl5ktq17u8/nTqJk+d1GqH3kP2nX+Lb6ReQKb3o+xcZaAAAA+NINo6xJguYHTY1xQXPytERBLTn+N15hPvuYt0u8QxadNtEpSfk2MsnjzzEL2MZPEPNFIU8SSdC0C6JD4G0Co2pHXt7BMPrU0Fq79olZMkmFxWaDX2WfRyRjkaBJ2yBJ0IzJcKd3wPPg9cU/oUMgtT7jjvlmjJjr+vmPErQjTr56YSnD7Hj51dgGS/cV8Xe3y6+gQyn1lVzJq+roSExrVjy/qXbZMi/ungMAAJwRhkvWPPZxW3TQZFm25O/nphM0Cf+br3KSyMsSRyabTbSLokMkiVMoGGEWEEeMEEnQtIs8mRyCIAmCLPCycPx/af93Xg8UK2vMkkma7F792C0uZlv9T+bTXkEPxne1MWxbt2xQQZO+Omh+xQvbXDfRIZDivhnfwQ/JvGRKwgqnzkjre0yaMRKT95Hvkk+Hu+cAAABnhmGRNWnQfNcbFzQnTT3XXB208O73WYFnRYEXeV4SOIk2Wwp2kbM5zAK83U6SJWcjezleJAU4TuR4geto+dQqoAfMhaQVla1sfG9Lg9kr6IXvFl98fZ82ztklRXS4Iu/rxu3mJLXR3jn1O2bXPLalMe4ueS7BSEwAAABnmKGfNT85cuhvcS2aX5t3zpTp5mo6tC8+ZXkuMvEcz3I8iZ48y6pmAbLAiTwncCzHcTxDJ45lOUYNhFV/2OgeNKhuSVPLzV5BVhvnj+N628z83obWn89mdqypuLXPM5QJ0aC5zFt6S8P6DVUDPtzZfDDhvXuz0/oFM6JGg88K6zHNW9x4pxEAAMCZYuhnza6O9p6e5iRozrp4/jmF08zVdJHYGE+n/xfstFboQuJASV+ZOaicGaWobOVqkimZbbsPWVt6FH13A33xz45lxaePmwesxx+fWRn96qA4RZfd6OrpmhOnbeO6HQxTWjzYLlZJMsdvuqXh7pXmg5sVGFkTAAAg9w39rHnu+SVF55eQBTNoTpycUmeZgfBjJ+qqRieFzFVN1TRF1RVFa//CLKCc/EwNa1pY1zVNU3VNZeibgzRGzLNxskBLpBY3fY2bPLFNmP2NuE6U370lqbhpJsXY4Y0SsPqG92kr7RnC8/b4Rzwzy/OgOX6T8Zhm2Y9qShkm0UBLAAAAkFuGxfOaRRfMInGzdPYlGQyahDjrYp2Ey7CiKqoaUrSgogQUpTsUPn6E7FWIT4/Q2+VBRQ2qZNJCqhbWSOH8C8aaNaTq4KMVC8azt15f+eASMpVdPL7iBTrgZT9D/0wlcZNmsoHjpjmO0gvfLbv1+kRT763q3tqM1wXRa7h1PGsM4WmMrGQVy4rGJfSTlt6/xDpLZHR33EkHAADIccMiaxIkbk6YNMVayRD7tQvUEImSIY1MgUA4EFb8oXBXKHxkfzgc7vriOPPZMaWLbAyrfiOGhlTFyJ3jruy93awqStKDyRdd9jN36Wxmx7b6F9aQyVs62/3YlphRMONNrXomibhp8O7Ylmiy9hpobfQhUeN1QfQadsx23XR/w/pPUhxZKVXmK4Jm19wf1XTa086KO+kAAAC5jE3nrTm5YPsxa+EroOvHb5jHHtsvjcwTRzjzCxTRKYkOiZcFbcZctf0427ZbC2lqUAl3h8MdoWBHOHgqoHHcZS/+q+iQWaMz0f8LLnY48nhBYOkjnAAQ42vjrAUAADhDIWumJfju5i9uqxDpOyod9nze5tAFu8RLHMfzfu4s+gRnKKwFQ+GugNIZUE52BNu7Clb8wXHjd6zjAWBAyJoAAGe64XIPPUvkOZfmLV6utHeFO7oD7UrXSSZwojtwwh8aOVkfNyV8oj10sjN0ojN8sosEzVCn33bT9xE0AQAAYPhAu2YGdNQ93PkfDwgOmZclwS5Io/O+tqFJkKQtC8vD+/dpwZAaCCtdfvvC2wt+vZLlkO8BkoV2TfgSqB+v7ji2ndVVlmNZ+ocjyCwy8ZGNxiDJvJM9p9o6EgCSgNyTAfmVd45++lV97NTQyfbAF52dh08cfuWFto1/8X+0N/RFe+iLDtUxZsTDz45Y+gj5ZWUdAwAAuYGX8gomujgpnza+0PYX44e53DMZP+hM9VuHAUBy0K6ZOboe3PxG98aXws3vacc/YXSNGzNWnPl39v9zve2q6xnBGFATAFKBdk34Mnz2Pwwrkn8Muz/drga/oC2YrNGiSf7wHENXeWM1Mi/8tXUgACQBWRMAcheyJjQ3NxcUFBQWFlrr2fDZf9NXa7ASwzv8n36gdLdZt9HNxMnTfElSJ1lmjHV2yt3WgQCQBNzSBQCAHEWC5s6dO71e78GDB61NWWHcHNf8jBqwn32xPLrUumNuTrRFhkxaZIFMg+CpZInKzL1/IuMVAmQLsiYAAOSiXbt2kaBJFnRdf+eddw4cOGBuz4JIgtS6GPWUlD/NfFgzfk7KGCvGIQCQLGRNAADIRfn5+WzkJRck423ZsmX//v3maob1pked0UKMrhrZkoZLK1nSZSNnGivGHACShawJAAC5qLCw0OVyRcfNrVu37tu3z1zNqEh81PWQGgyEA0bINOIm2WMt0CU6WYkTAJKFrAkAADmKxM2ysjIuMlocyXxNTU0Zj5sBJbjuw7dub3h47poflzx9e+lT375849N3eNc/5/tbO8mdkYhpZEwETYCUIWsCAEDumjx5clzc3Lp16969e83V9L3SvPaqtXcu37pqy4kPg7awY6TsGGlTndrO4LEan/fajc+sad1uNnLSFk2rdRMAUoCsCQAAOW3SpEnRcZNoamry+XzWymBpura8YfGShts6+G57geQokB35kp1Ost0p2vMEe76g2rXf79lc5X05qIatpk2zfTMNPk9tZRntQ06VlVV6En6QmFL9F4tm9EwvqyXlMnMKXy3dS3u6+zxWwZ5u73RL9LG16A8P/Tvjx9cEAIDh4MiRI5s3b9Y0zVpnmDlz5hQVFVkrqXvgtZ89v7NOcsqSrIsyL0o8J9BRNRmWU0JduqKHFU0NaaGAWjaxnD3V+qjrBo43xtucdp9VRQpIEKyoZ9w1Nc3V1V7G5XaXMExzfb2X7nPVtDZWRX8QEuQqjF0x5WKLWRU26HXl0VtcNQ0L11X0nqK53tv3WCKJU5CsWVztdTc0MBXkTAbzdOapew6lp+hTP0AvtGsCAMAZKZ22ktc/evmPf/uD5JBlh81GWzElMjmckiNPppNDkJ2Cncwdwk8vfeDvJ8/bdPyjNR9tM+6fp9NAU19dzdS06npjHdWot9a4yFZv9cqoZkES8WgKJOmtt5xR0Fu9iDZaDsxbXVFd0tB7qHWO2GNTOUXzihUkwJKCFM21vtoVJGjSTT2n0FuXXmcVB+gLWRMAAHLd4cOH+zZqFhcXWyspUjTlN2/8QnRINGs6bbJdlB2izUnjpp3OZZtTsJGgaRdumVW1YMatz3/4mGjjH9/T2B7003wbGPxIn+6GmOa/oqrVRtpsbulJeJ6V1d4+5UjBpe74UNoPd0Ndeeyhxjmijk3pFF5vSWzB1t20BbRkRsyx5Wk0MMOQh6wJAAA5jQTNxsbGDN4939Sy4Wh3myiLok0ik2QnsVK02US7w2jadMg2m0Cmr0++4Y6Zy1ftWRlkO3mZC7DKiwd2MoyuHV/LhD6x6kqNe0HkhndE0YwSa8nkWW80Gi6JL8eULyBJMDqU9qPvKZii6xZGB9rUTtGnYPFMWln9iiSeIAUwIGsCAEDuamtriw6aLMvOnTs3naBJvNmygRN5XhQEURAlUZQEmUw2GjcddolMNlkoGl1658zHjwXaXj/6nChygsjxIvvXI0a80gL68TVGTRnna2kmc291sdnpJlrkmcmBuWYmaOyNCbTpnqKoanWD28V46ytIDUl1WoLhDlkTAAByFAmaXq83Lmiee+655uqg7Tr2Pi/wkYkETtoxSJJ4WRbtsmS3SWc5J/yyeJXIyfW+u3UubPYZ4nh298ljVhVawFrIDpe7HwtnWCXSluwpYm+WG4rK6aOmJHCSxGlETgROGBCyJgAA5KJDhw7FtWjOmzdv+vTp5mo6Pu86znIsnXgSIo0cyXE8z0kCJ4tigVywuPCpkcLYphMbd5x6y9hLgyaZd6shvxI2ugel00NoAGYLpGvhEtrrJoGqmEcxk2U2ZVqpMUOnoIGT9gqy2jiT6LYEwxayJgAA5KL29vaenuYkaM6fP3/atGnmatqs915G0OxIN+kMzwk3j1heKF8QULuePbzM3B2dLOm/mlnKmQbjaUjvulcHm9wSHWr2Beq5u57uKWKQyGl2Pdrdam0BiIesCQAAuajEQBbMoDl16lRze/rG5U/UNX2cY+J5I0o0StdUTVFVTeW/VbDsPHGuzmirjtzzhf9jUozsVTVGU+lCgWiTeYFWkbW4GekNXtzntrTPU1mZROOht3pRzKHkMOMxTPfSSGfy9E7h83jiDjM7pgP0C1kTAABy1KxZs0jcvOSSSzIYNInSCbPtnOORy1f/YOYvScoMK0o4pLKKeNvYhy6yXUEKbD3xl8bjLyiKZk66YuZRvWTUeLOGbCqvM0bEjHS9oYxX9BRX1NM74afhqqkpMR6hNI4kx5HD6NbWniHfibRO0bIi6jB6BnoCOtCStR8gHrImAADkLhI3p0yZYq1kyNXnL1DDaoE8snTMnO9N/5ES1ArYsXdNW1XqvIzsPR44/MTeuwKBcCiohkJqOKSRSQlpalj/xsTeTt6KovTc4s+woqrG1oYaq+sN5fW6XO6ahuRezXNdHT2YMY6kt84THjj4UxRdtzTqsHp6WENrTJIFiIN3VAIAwPBC/uG78ck5M84pebj8GbJ6oGPPOMdkh5BHloNaYMn2aw91fqQqmhKmKTPoV4N+JdCp5oWl1yrcDlE2OgrxJwsW5+XlCYLAsnFPf35VrHdU4mWRkGvQrgkAAMMLSYf3XP17T/P//HnXarI8reACM2iqmvLgB3fs7+5UmIlhZnxYPzusjgqTkBmyKSH+rqv/kH/+Sv7cFdy0+9gpy0aNGiWKYs4ETYDchawJAADDztwpl1Vfeu99G6s9LS+Y9/dI0Hyg6d8aD27s7ujq7uwKdHT7O7sDXf5gZyDUHVx44W03lnzHPBYAUoKsCQAAw5Hbdefi+UvvfGnR797+9Qn/5z97c9FrLX/u7gh1nezsPtnp7+gOtPsDHf5gR+DmC+9Ydk2NdRgApAhZEwAAhikSN1f9s6ex5Y3L/1D05u4N3Z2sv0MJtHeToNl9qossnMWOq73h+XuufpRj8c8lwCChbxAAAAxrOqM37ntz40frdxzddrzrqK5rZznGzRx30Tdm3HBl8XUCZwyoeQZA3yDIUciaAAAAAJAtuCkAAAAAANmCrAkAAAAA2YKsCQAAAADZguc1M0P9eHXHse2srrIcy9I/HEFmkYmPbOQZsso72XOqrSMBAAAAhi60a2YGL+UVTHRxUj7N7jS+Gz/M5Z7J+EFnqt86DAAAAGBIQ9bMFJ0TbHnj5wu2MWa0tMKlFTE1MqeTsWpMAAAAAEPfsMiazc3Nhw4dslayheTIIMvqzgllorPQCJlm5tR1zZjTZVrMWEDWBAAAgGFh6GdNEjR37tzp9XoPHjxobcoKI0FqfkYN2M++WB5dSmOl1YRphksyaZEFMgEAAAAMfUM8a+7atYsETbKg6/o777xz4MABc3sWRBKk1sWop6T8aWb7ZfyclDFWjEMAAAAAhrghnjXz8/NZljWXScbbsmXL/v37zdUM602POqOFGF01siUNl1aypMtGzjRWjHnqfJ7ayrKyWp+1mllZrRwAAACGpSGeNQsLC10uV3Tc3Lp16759+8zVjIrER10PqcFAOGCETCNukj3WAl2ik5U4U+Z7dUV1vddaybSsVg4AAADD09B/XpPEzbKyMo6zPinJfE1NTRmPmwEluO7Dt25veHjumh+XPH176VPfvnzj03d41z/n+1s7yZ2RiGlkzEEGTQAAAIAzztDPmsTkyZPj4ubWrVv37t1rrqbvlea1V629c/nWVVtOfBi0hR0jZcdIm+rUdgaP1fi81258Zk3rdrORk7ZoWq2bAAAAAEPfsMiaxKRJk6LjJtHU1OTzpftsoqZryxsWL2m4rYPvthdIjgLZkS/Z6STbnaI9T7DnC6pd+/2ezVXel4Nq2GraNNs3U+GrLWNZtria3uP2VheTZSL22UrjcUtzB1FWVunp++l8nsqy6DK1HmPraSpPfBQAAADAafH33nuvtTjUFRQUjBo1qq2tzbqVzTAff/yx3W4fPXq0uToIKzb+fO2uJ+U8m+zgZYdIJtEuSjZBtImcoAoCxwosz7Mk0k8d63pz37vXTD7PeonlqCutKpLzxYnOoLPwYvm999oYxuV2l19MXFFWNr/YvHiSBuf/7Amy09wnv7fe+94f//31Uf/yr/N7Pp2nkp3/s/fa2qwi8nteb/scWmDAyvs9yqoVAAAAoH/D633oR44c2bx5s6Zp1jrDzJ49u7i42FpJ0esfvbx4/c1yvk122mSbQiOmxPM8x/Ecw3JKoF1RdTWshUPa7TOXhsPqo5vu+fkFVyy6YC5HTLvfqiUVvtqy4mqvq6a1sarI2kSZm5nY7fFlEx3r8/iY8iJrNWHlpz0KAAAAYADD5R46cfjw4bigOWfOnEEHTUVTfvPGL0SHJDlkmjXttFHT5pTseZKdzmWbU7A5BNku3DKrasGMW5//8DHRxj++p7E96KfxPpC5kT49K+ndb3dDdEZkmKKqpW56S3yldcO7dTe9RV4yI6bMaSPj4I4CAAAAMA2XrEmCZmNjY1zQLEojM21q2XC0u02URdEmkUmyk1gp2myi3SE5nJLDIdtsApm+PvmGO2YuX7VnZZDt5GUuwCovHtjJMLp2fC0T+sSqKz2e9fUM46pZUm6t9yhfQMIm09xiPndZPNNF5vUrEj3G2b/BHQUAAABgGhZZs62tLTposiw7d+7cdIIm8WbLBk7keVEQREGURFESZDLZaNx02CUy2WShaHTpnTMfPxZoe/3oc6LICSLHi+xfjxipTQvox9cYNaXJ19JM5j1deqJVkBDao6hqdYPbxXjrK0jJxD2HEhjcUQAAAACmof+8JgmaXq83LmhOnz7dXB2065+4+FBwn2OE017gsOc7HXKX3UmDpiyLsiAwnMB1q3dPWz9SGHv/zn/Z+vHr/i6luyMc6Axz3dy73/oZy/F0KrzHqi45CZ+eNLbRLj0l1pZYMxcsqSrvjdU+T+WiFfVeemecHNOwuq5nX8LnNS39HwUAcKZTP17dcWw7q6tW302WPlRPZpGJj2zkGbLKO9lzqq0jASAJQ7xd89ChQ3EtmvPmzUs/aBKfdx03fvmwLE9/Kxm/kTie5ySBk0WxQC5YXPgUCZpNJzbuOPVWz+8rMu9WQ34lbAyxmZGUXzSDZkzXwiV1iUUHTaKovK6xUddbrdbKRcm9knJwRwEAnAl4Ka9goouT8q1RkM2RkM3lnsn4QWeq3zoMAJIzxLNme3t7T8MtCZrz58+fNm2auZo2672XEfQ3Ed2kMzwn3DxieaF8QUDtevbwMnO3UcBCv/TetXQZj1R6172aUvwj4XF1DT1ud6u1JRmDOwoAIMfpnGDLGz9fsI0xo6UVLs05oxm/sq2NxgQAKRjiWbPEQBbMoDl16lRze/rG5U/UNX2cY+J5I0o0StdUTVFVTeW/VbDsPHEu+fW06sg9X/g/JsXIXlVjNJUuFIg2mRdoFYP9fRWX9CIdzov7PEzp81RWRlogfR5P3G6zi3m82MqTPQoAIEuam5sPHTpkrWQLyZFBltWdE8pEZ6ERMs3MqZNf4JFVWsxYQNYESM3Q7xs0a9YsEjcvueSSDAZNonTCbDvneOTy1T+Y+UuSMsOKEg6prCLeNvahi2xXkAJbT/yl8fgLiqKZk66YeVQvGTXerGEQiq5baHQLrygrq6ysLIsEyfK6VtraGOm/Qxnv+SmuqKf9hkwtK6J20xcM0Z5D7oa6nu7rCSs/7VEAANlDgubOnTu9Xu/BgwetTVlhJEjNz6gB+9kXy6NLaayk6dKYaLgkkxZZIBMApGBYvDdo3LhxI0eOtFYyROQlz4d//te51dNHnMcx/Aef/u/Z9ol3zXjyfOccsvd44PDvPrq1K9AZDmmhkBoKaqGASqZwQPv+ubNLxkwwnzRX86xXP5p1nt7o+f867+jrf3zP2/bee++1XfyTn9xgvjeIbP+XeaOC+9vf83rJHrKvzeVy/7xubf2/WE9rjj5r4tH39kd2v0d3121a+9N5Ua//SVB50emPAgDIjl27dpGgaS4fPnw4Ly8v47/JLd3kf5YbCVIPM4zK28YHT+4xfjOTGWvMInPGWBh5uXEYACRleL03KIPI93bjk3NmnFPycPkzZPVAx55xjskOIY8sB7XAku3XHur8SFU0xXhvUNCvBv1KoFPNC0uvVbgdomz2Qz9ZsJj89hQEwfgVBgAAvQ4dOuT1env+kSK/J+fNm5e5Z+6jfPqnSIMlmXhGGNV+8JWeXp/kdzX5Q183TOZGH09uSs+D+MOAz1O7csW6masTDFECgzAsv8+hfw89S8hvvXuu/r2n+X/+vGs1WZ5WcIEZNFVNefCDO/Z3dyrMxDAzPqyfHVZHhUnIDNmUEH/X1X/IP38lf+4Kbtp97JRlo0aNEkURQRMAoK/CwkKXy9XzG5KEzq1bt+7bt89czahIm4uuh9RgIBygt8vJ+ejcOLGxahQzF4YR36srquvxnH7GDM/vE1lz8OZOuaz60nvv21jtaXmB/iYyguYDTf/WeHBjd0dXd2dXoKPb39kd6PIHOwOh7uDCC2+7seQ75rEAAHBaJG6WlZVxnPVPFflN29TUlPG4GVCC6z586/aGh+eu+XHJ07eXPvXtyzc+fYd3/XO+v7WT3BmJmEbGHF5BEyAjkDXT4nbduXj+0jtfWvS7t399wv/5z95c9FrLn7s7Ql0nO7tPdvo7ugPt/kCHP9gRuPnCO5ZdU2MdBgAAyZk8eXJc3Ny6devevXvN1fS90rz2qrV3Lt+6asuJD4O2sGOk7BhpU53azuCxGp/32o3PrGndbjZy0hZNGjgRNwFSg6yZLhI3V/2zp7Hljcv/UPTm7g3dnay/Qwm0d5Og2X2qiyycxY6rveH5e65+lGPxbQMApGzSpEnRcZNoamry+WIHZEudpmvLGxYvabitg++2F0iOAtmRL9npJNudoj1PsOcLql37/Z7NVd6Xg2rYato02zcHyeepNccJMZWVVdb2efWvp5LuoUOBGKXNkkbZxG8JjimVoJivlu6t9NCSVkG6Yu6Lu57oyzGPoy+mM4bVs0rEvMojiY8z0NlPy0ePia0/7rMl+31aVxD9fcYUNQvFfrqI3gp6DOY7r03y+zR3EKn+VcZ+E7XR1/uVo/9jDdJGfme9vff1exp+9A9PzbvkscL5tZOuf+LiX7z8rxv3rA+rYasQAAAM1uHDh9euXft8lNbWVmvfoNz/l5+ev9JR+vio2U+NvOS5MV9fN/aKF8ZftX7CVS+d8/X/HnnZf42YvyZ/zlPOv/tP2w9f/McfrSoJtvwqvPdudd8y6/hUGYPTGVxuQ+9qg1XE0OCm22oazOJmWZdV1lUT95Hp29zMPWY5ayW6mHled4NRr8k8YeSCXLHniFxNa0NNb5XWJbtrGiI1J/lx+j37afV8tOjPFvXJUvo+GXeNUTzyWekmorekWVuf75ewju+tc5DfOfkb7S1uXXLU95nWX2Vkg3UoLZXoo3xlkDUBAOAM0DdrtrS0WPtSt3HPS+f91l76n6PmrJpw6dqzScS8pmFSxWuF178x9fo3p1/76lnfeHnMlX8edel/jVj1wcontv32gt+JT77yzbCPZM17rCpSY6UBd2+2oFp7NlsbiJ4gEVvWilbRESLBpsjGqG3WBhJA4uJHK8k+cZcTf2zCTVTSH6ffsw/MPIzGxuiDSPyNrKf+fboSfK6oi+rng8Zv7nMc1edYa0OCTz3gaQZZbaI6WxvS+19imYWsCQAAua6trS2DjZphNXzlf5xX8tiI2c+Mv/S/p/2flyaWbyz8h/83/VuNRf/0zox/2nrBgrfGX//GuGsbzq5rXnbS/9mVayaWPmb72krH57t+puy9R/fvtypKmhUloiNQRJ+gkCAumeIrMQv2LRe33Tos4cnjmWWjS/a5PCr+SqLEl0/l7L36+2gRKVxAv9+nub3PlcaWi68t7e+8z+UZ0qu2v6NzB54gBACAnHb48OHGxkZN06x1hpkzZ05R0eCHJ9zUsuFod5soi6JNIpNkF2S7aLOJdofkcEoOh2yzCWT6+uQb7pi5fNWelUG2k5e5AKu8eGAnw+ja8bVM6BOrruQYr/d11SxJ8M41851t3nWvxj6a517Qp6z1drfmFrOkZ3194jrLF9DsESlmSXxywufx1NbSF8MR1tOEp5Xyx+n37IkZHy3RVxCRie/T/Jp6mS98ZurXRz3o6Ht1HTmRe2lkLMwMfefx0qy2eCb976J+RT8P9OYAZE0AAMhdbW1t0UGTZdm5c+emEzSJN1s2cCLPi4IgCqIkipIgk8lG46bDLpHJJgtFo0vvnPn4sUDb60efE0VOEDleZP96xPjHXAvox9cYNSXJ10JfGFwyI+FVF80osZaiuGYWW0tRYkqadfZ0MolGXyacBLN/SXFFRXV1vfFG45LeBxkHlPrHSY1Z/wBRM0sXYCa7qLDpWUmzd++FpP+dJ5RutUVVq+nDnt7Im6pzL3IiawIAQI4iQdPr9cYFzXPPPddcHbRdx97nBT4ykcDJixIvSbwsi3ZZstuks5wTflm8SuTket/dOhfmBOMVQjy7++QxqwotYC181SKdTPpYOMMqYeoTzHy1iyrqvYzLegKSBPrGurolRstp5vUTC3NN+RJ6k7onbPbX3Djo73xgaVRbVF7XSB9WdZHEaUTOHAucyJoAAJCLDh06FNeiOW/evOnTp5ur6fi86zjL0VdOsjwJkUaO5Die5ySBk0WxQC5YXPjUSGFs04mNO069FXlRJZ13qyG/EjaG2BzEyEdxt0IjBmykixFT0my+cy1cUpdYVfmAFVp3hxsaY8oZ96aTlPbHOY1+6u+VhQswb8DXrzBGIjKj5sLreitK8zvvT4aqpYGzUTciJ23jXJRwAKevBrImAADkovb2dj0ymCUJmvPnz8/cy9Dj3gxMsyPdpDM8J9w8YnmhfEFA7Xr2cM97z3uTJf1XM+WcaT1DWL0ywZiHZuiLv2fe53lDwryj21PSeEovUbmkJEyVZkw7rdQ/TmoGqt+QvQuwaibfqq92BYmavY9qGtL7zvuV0WpJ5FxNm2e9u1utLV89ZE0AAMhFJQayYAbNqVOnmtvTNy5/oq7p4xwTzxtRolG6pmqKqmoq/62CZeeJc3VGW3Xkni/8H9PBkzVd1RhNpQsFok3mBVpFinEz0u+kIm60cJ+n0uiOExdpCG/1opjboKSk8exeb0mzTm91cZ/bpaRs/KDnfVj9SaI6wpCjFvXXNSgut6T+cVLTf/215obsXYD1ra5budJo9417ZjS977xXwu9zsNX6PJ64w1Jpnv5SIGsCAECOmjVrFombl1xySQaDJlE6Ybadczxy+eofzPwlSZlhRQmHVFYRbxv70EW2K0iBrSf+0nj8BUXRzElXzDyql4wab9aQuvI6Y8AaowNIWVklZXTMMZ4IbK3r80hgTU2J8dydUbSfkladkS4hRkn66hhS9rTtk2YDHjm25xTkqBJzwPNoPeWMYj0v7knx46Ssv/rXWfuzeAFGDyFvvfEs65I+9aTznVMDf5+Dq7ZlRdRh9Gug34K7If2/hcyhjwQDAAAMG2/vff3imnHHO4+S5T/66ipeK7xj66V7O3eae4/52257d9a33574D5vGX+s5+8oXRrvWFPz9HxwzH5b+6ClX6HDuy7QD94XDYZpAU9Jqdt+IcLmi3xsTYYyWSAdgpC/u6SmcsChllOqts2+lRohJOPqi8WSfhRxFDkpcNqpYzNjpyXyc/s9+en3rjxugPPnvM9EV9HzR8cwjEu4ypfGdU/1+n4OsNvZbSPA9feVY8se6OgAAgGGA/MN345NzZpxT8nD5M2T1QMeecY7JDiGPLAe1wJLt1x7q/EhVNCWshUNa0K8G/UqgU80LS69VuB2ibHQU4k8WLM7LyxMEgWXjnv5Mk6eSragnOacxvfvQADkDWRMAAIadpoNvf/9P5fdX/Me3S75vbWIYVVPu++DW9zs+1DVdIULhcCAU9AeCnXT67TVP3FjyHatoFg35rOnzeVr777ZSXFye3uipkHuQNQEAYDiq9z7871tW/O76p68t/keWZUnQfKDp397+5BVGHE3+ZdTCajgcVoLhUHeQTP90wa33XvuYdWR2DfmsaXxAa7kvd4OeSw8aQiagbxAAAAxHbtedi+cvvfOlRb97+9cn/J//7M1Fr7X8ubsj1HWys/tkp7+jO9DuD3T4gx2Bmy+8Y9k1NdZhkK7yOpLl+4WgOQQhawIAwDBF4uaqf/Y0trxx+R+K3ty9obuT9XcogfZuEjS7T3WRhbPYcbU3PH/P1Y9yLP65BBgk3EMHAIBhTWf0xn1vbvxo/Y6j2453HdV17SzHuJnjLvrGjBuuLL5O4IwBNb886BsEQw2yJgAAAABkC24KAAAAAEC2IGsCAAAAQLYgawIAAABAtuB5zcxQP17dcWw7q6ssx7L0D0eQWWTiIxt5hqzyTvacautIAAAAgKEL7ZqZwUt5BRNdnJRPszuN78YPc7lnMn7Qmeq3DgMAAAAY0pA1M0XnBFve+PmCbYwZLa1waUVMjczpZKwaEwAAAMDQNyyyZnNz86FDh6yVbCE5MsiyunNCmegsNEKmmTl1XTPmdJkWMxaQNQEAAGBYyN3nNbcfsxYG9rVx1kJ/SNDcuXMny7KXXHLJlClTrK0Z99k6I0FqDOdk+LxQx77QiZ30+Uy295FNusbS5zU5lmenLLUOhDOV+Urfr+rVvV/t2b8aCX8nnPY3AAAAfLWGeLvmrl27SNAkCyRSv/POOwcOHDC3ZwEJmkZq17oY9ZSUP81sv4yf0+ZPumIcAgAAADDEDfGsmZ+fz7KsuUwy3pYtW/bv32+uZlhvetQZLcToqpEtabi0kiVdNnKmsWLMU+fz1FaWldX6rNXMymrlwwS+QwAAgFhDPGsWFha6XK7ouLl169Z9+/aZqxkViY+6HlKDgXDACJlG3CR7rAW6RCcrcabM9+qK6nqvtZJpWa18mMB3CAAAEGfo9w0icbOsrIzjrE9KMl9TU1PG42ZACa778K3bGx6eu+bHJU/fXvrUty/f+PQd3vXP+f7WTnJnJGIaGXOQQRMAAADgjDP0syYxefLkuLi5devWvXv3mqvpe6V57VVr71y+ddWWEx8GbWHHSNkx0qY6tZ3BYzU+77Ubn1nTut1s5KQtmlbrJgAAAMDQNyyyJjFp0qTouEk0NTX5fOk+V6fp2vKGxUsabuvgu+0FkqNAduRLdjrJdqdozxPs+YJq136/Z3OV9+WgGraaNs32zVT4astYli2upvdnvdXFZJmIfS7QeFTQ3EGUlVV6+n46n6eyLLpMrcfYeprKEx+VNPMZRuto48pq+1yap5LuqiQVx3yMBEWTK2l+JKNYtN6De9aMz0k/Id1D1yt7PnjU1ujNCSX7F9RTYeLvwZR8yThp/k0BAABk3nDJmgSJm5deeml03Hz33XfTjJsPbrxz3e6n5XybLU8iE0mZtnyZJs4C2ZYv2PNEOU+wOQUpjxdGX/irdzcaGXNQjZozFroJl7HsoovEwhnGKkUyRnEFfVTQ3OdivN76iuLYrEOiVXFFvddrFaFl1rUYBQaqvP+jkkEjmHFh1vHmlVWTS4uPgYaWWvoxmhmrKGMUHUzJoqrVNWRr/Yrob8BTWVHPMO6GuFGCWmlVZk1mRcU0jRrfaH10/XFfZ5yB/4KS/x5S/MaipPc3BQAAkCX0zm5Oeu+TpKaUHD58eO3atc9HaWlpsfalbuOel877rb30P0fNWTXh0rVnX7V+wjUNkypeK7z+janXvzn92lfP+sbLY67886hL/2vEqg9WPrHttxf8TnzylW+GfXer++6xqkhRK81PjKum1Vq3mJvjtseXTXRsa0Nr72rCyk971IAa3ORgmu5i6+zZbG0grE2MK+ZUiT5YqiV7NplFEp406iirIirB5uijrcNjNpnFYq6Csk6U/PeQdMneLYlOnfzf1Jkh7v/3zQkAAHLcMGrXJEFz8+bNmqZZ6wwzZ86c4uJiayVFiqb85o1fiA5Jcsiy0ybbRdkh2pySPU+y07lscwo2hyDbhVtmVS2YcevzHz4m2vjH9zS2B/30FnogcyN9elbSO7fuhsaqImsLVVS1lKQRb/VKqzWsdTe9vVsyI6ZMeVH0agKDO8rkq11RT37QdsSY8kXldUYwim10NLiXxnwI8zMw3nWv9mmeS6JkeR3NY9Y34KtdRL6lPm2ahKtmdVRNVkV9Nl+3kEa5+vWnaVtMJPnvYRDfWI90/qYAAACyZ7hkTRI0Gxsb44JmURr/Em9q2XC0u02URdEmkUmyk1gp2myi3SE5nJLDIdtsApm+PvmGO2YuX7VnZZDt5GUuwCovHtjJMLp2fC0T+sSqKz2e9SSfuGqW9AlR5Qtoamq2bqMWzzTC0opEj3H2b3BHmYz0k+jCItGtT4Z0L4gva36GvpIraaZNktA8/SbN+HgW+ciuhdfFxr0ZJdZSypL/HlL/xnql8zcFAACQPcMia7a1tUUHTZZl586dm07QJN5s2cCJPC8KgiiIkihKgkwmG42bDrtEJpssFI0uvXPm48cCba8ffU4UOUHkeJH96xEjC2gB/fgao6Y0+VqaybynO0o0+nRij6Kq1Q302b/6ClIycc+hBAZ3lMG8sPgkZ0kjuqXESJve6op+kybjmplcy7aZ5QYh+e8hrW8sjb8pAACALBr6WZMETa/XGxc0zz33XHN10HYde58X+MhEAicvSrwk8bIs2mXJbpPOck74ZfEqkZPrfXfrXJgT6HvQOZ7dfTLyUmctYC1kQqQ7Sh+9/VOKyusa6aN/Rq8RGkmSCiSDOyp3DDojnnHO9L8pgK+M+vHqk+9Xndr+4/a/Le74oKpzx0+7m3/u370ksOeXwZZfhXxLw3uXKfvu1Q48oB16UD9SYx0GAMkZ4lnz0KFDcS2a8+bNmz59urmajs+7jrMcSyeehEgjR3Icz3OSwMmiWCAXLC58aqQwtunExh2n3jL20qBJ5t1qyK+Ejd7og+qQHs9s73ItXFKXWFWfR/8aG43eJkYb2KKBulb3GtxRVOQWfpwBm/AyyHpM001vpVecriN3NiX/PaT3jQ3+bwpg+OKlvIKJLk7K1yNDIMeMiGxOxg86U/3WYQCQnCGeNdvb2+nvCwMJmvPnz582bZq5mjbrvZcR9DcR3aQzPCfcPGJ5oXxBQO169vAyc7dRwEK/9N61dBkNdwM8yJcQiSTGoEDe3a3WlmSkdJT1hGFP56RovlfX0ScTk7x9PWieSjrgpbuBRG7jwc2vJG0m/z1k8Bsb3N8vwLClc4Itb/x8wTbGjJZWuDTnjGb8yrY2GhMApGCIZ80SA1kwg+bUqVPN7ekblz9R1/RxjonnjSjRKF1TNUVVNZX/VsGy88S55NfTqiP3fOH/mBQje1WN0VS6UCDaZF6gVQz291Vcfoh0OC/uc8fU56nsGYHc5/HE7TY7LseLrTzZoxKyunTXV8SNS0kui0ZAxr00puN8xsUMp1m+xOjI/aWkzYR/Qcl8D2l8Y2n9TQHksubm5kOHDlkr2UJyZJBldeeEMtFZaIRMM3Pq5Bd4ZJUWMxaQNQFSM/Sf15w1axaJm5dcckkGgyZROmG2nXM8cvnqH8z8JUmZYUUJh1RWEW8b+9BFtitIga0n/tJ4/AVF0cxJV8w8qpeMGm/WMAhmwxfNImWVlZU9r7KJjIhj9QqhjLfHFFfU0/uuppYVUbvp+3DixzVPWPlpjxqQdWFGx6XIhRmXxTCumtYkKxkkM2n2duouskZ3z2raHPgvKInvYfDfWHp/UwA5igTNnTt3er3egwcPWpuywkiQmp9RA/azL5ZHl9JYSdOlMdFwSSYtskAmAEjB0M+aBImbU6ZMsVYy5OrzF6hhtUAeWTpmzvem/0gJagXs2LumrSp1Xkb2Hg8cfmLvXYFAOBRUQyE1HNLIpIQ0Nax/Y2LvbVBFUej/Sk5eUVUjfRCP9v2or/cyvd1+qhpbG2qsXiGU1+tyuWsaWnsGoSy6bmnU7nq6u6E1NrwkqDyJowZGL6ynu0qkCnJdeuxgoBkXSZoJhs7Matoc6C8oue9hkN9Y2n9TALln165dJGiSBfJ78p133jlwIHPDEseLJEiti1FPSfnTzPbL+DkpY6wYhwBAsljj/39y0fZId+2BfW2ctfAlI9/bjU/OmXFOycPlz5DVAx17xjkmO4Q8shzUAku2X3uo8yNV0ZQwTZlBvxr0K4FONS8svVbhdoiy0VGIP1mwOC8vTxAElo17+hMA4iX8nfBV/QaAL8GhQ4e8Xm/PP1Jm587MPXMf5dM/ReImmXhGGNV+8JWeXp/kdzX5w/I87Q1q9PHkpvQ8iJ+rfJ7alSvWzVyd5f9ZD5AUZM3Bazr49vf/VH5/xX98u+T71iaGUTXlvg9ufb/jQ13TFSIUDgdCQX8g2Emn317zxI0l37GKntl8Pk9r//1OiovxyhrINGTNYahv3Jw7d25GxhKJ8ela44fO6FpIUzVuZOjIRjNocuYAIuQPyZpG7qTLU5cb5XOXr7asuNrrqum9twXwFULWTEu99+F/37Lid9c/fW3xP5JfgiRoPtD0b29/8gojjiZfrBZWw+GwEgyHuoNk+qcLbr332sesI894nsrYseJjuRt03L+FDEPWHJ4SjpGc2bgZOLr65dZ3Nu5/d8dn+04GuxiWcXLC+QVnXTF++oJpF46Q7TRfcjzD0yHu6EB3yJoAqUDWTBeJm7WN931v3o/dc+68d/NPmo42kN9TOpdH77JruhIymja7Q98pdd/9zYfJ/yi2DgOAFCFrDlt9XzKckVdymF5pXvub1xefUDp4+mo3EiVZkmc1NaQpuhrWbbrww/Pn3zLjYo7lWTNrciRr3msdnKuQNSGnIPqky+26c9U/expb3rj8D0Vv7t7Q3cn6O5RAe7e/o7v7VBdZOIsdV3vD8/dc/SiCJgDAIEyaNKmsrIzkPGudYZqamny+2GG+Uqfp2vKGxUsabuvgu+0FkqNAduRLdjrJdqdozxPs+YJq136/Z3OV9+WgGjY6BtGn9c3DU+SpJDHWGM/M56mlY0WYyvq84IvkRLKd9mH0eaxy0R0ajaN7DqfH10ZXYB5tjJJmjmhhFIoZRu00NQBkHNo1M0Nn9MZ9b278aP2Oo9uOdx3Vde0sx7iZ4y76xowbriy+TuCMATUBIA1o1xzmjhw5snnz5ujWzTlz5hSl8WT4A6/97PmddZJTlmRdlOl7hs2XCTMsp4S6dEUPK7R9MxRQyyaWs6daH3XdwPFkP89Nu8+qIgXGc0eumoaF6ypIEHS53SV0SKd6Lw2FMQ2QZpuku6GBqYg8qBR5KsncRbeYxzPNdMALY7Wh0XxuiXYKWr/b2mGVYmYuWGK9Qi6JGgAyDlkTAM4MyJrDXN+sOXv27OLiQb5+7PWPXl68/mY53yY7bbJNkWwCyZr0JjnP0awZaFdUXTUGErl95tJwWH100z0/v+CKRRfMJWmUm3a/VUsKep5xdze01vW+O9gKf1Fp09zicrm8zMKYe+BWDbHH08ZPYwjemKfkrTri76GnUANABuGuLgAA5LrDhw/3bdQcdNBUNOU3b/xCdEiSQ6ZZ0y7KDtHmlOx5kp3OZZtTsDkE2S7cMqtqwYxbn//wMdHGP76nsT3op+0zgcGP9EnfsBAd/4rM10z0eTmt11vSEBMUfbUraFSNP56+kdZ8m8eKmBvlCaRfA8DgIGsCAEBO69s3KM2755taNhztbhNlUbRJZJLsJFaKNptod0gOp+RwyDabQKavT77hjpnLV+1ZGWQ7eZkLsMqLB3YyjK4dX8uEPrHqSo17QZ+WQ+t9Y80tMUGv96VnFuO1s322GswavOteHTgqpl8DwOAgawIAQO5qa2uLDpqsMeZROkGTeLNlAyfyvCgIoiBKoigJMplsNG467BKZbLJQNLr0zpmPHwu0vX70OVHkBNpLnf3rESOMaQH9+BqjphS5ZiZoiS2aYTxUOTBfC33lcMmMhJ/7S6oBYJCQNQEAIEclHFwz/dGOdh17nxf4yEQCJ+0YJEm8LIt2WbLbpLOcE35ZvErk5Hrf3ToXNvsMcTy7+2TkqWEtYC1kST+ZEOBMhKwJAAC56NChQ3EtmvPmzcvIKO6fdx1n6cuA6LjsBmOsdp6TBE4WxQK5YHHhUyOFsU0nNu449Zax13w5JduthvxK2HiVZea61Q7Y4Bgr7kZ7xJdaA0DKkDUBACAXtbe394yUQoLm/PnzM/cydNb6aaHZkW7SGZ4Tbh6xvFC+IKB2PXu4573nvcmS/qs56JyZ6IlIz0o6CFHCu+tRrCcq47sQGXyvrvtSagAYJGRNAADIRSUGsmAGzalTp5rb0zcuf6Ku6eMcE88bUaJRuqZqiqpqKv+tgmXniXN1Rlt15J4v/B+TYmSvqjGaShcKRJvMG+MlDzJueqsXxYzd7vNUGiMhuZfGDE6UQFHVUjf5UV8ROzC7MWIRDasJavDubrWWDIOoASAjkDUBACBHzZo1i8TNSy65JINBkyidMNvOOR65fPUPZv6SpMywooRDKquIt4196CLbFaTA1hN/aTz+gqJo5qQrZh7VS0aNN2sYJFdNTUl9RTF9Vw9RxrLGwJaumtZkBra0hiYy3gZkVtBvDVbfdhIrabmySitcplADQAYhawIAQO4icXPKlCnWSoZcff4CNawWyCNLx8z53vQfKUGtgB1717RVpc7LyN7jgcNP7L0rEAiHgmoopIZDdER3JaSpYf0bE3vvMiuKMoiXoVxX19pQ42a89QS9be2uaUj+peVFVY2trQ1ul4vxGhXUe11GBXqfGkhJUo7ESlrOy8ywNqdQA0Dm4L1BAHBmwHuDIFPIP3w3PjlnxjklD5c/Q1YPdOwZ55jsEPLIclALLNl+7aHOj1RFU4z3BgX9atCvBDrVvLD0WoXbIcpGRyH+ZMHivLw8QRBYNu7pz4Ssd1QmHywBhgy0awIAwPBC0uE9V//e0/w/f961mixPK7jADJqqpjz4wR37uzsVZmKYGR/Wzw6ro8IkZIZsSoi/6+o/5J+/kj93BTftPnbKslGjRomimFzQBBjWkDUBAGDYmTvlsupL771vY7Wn5QXz/h4Jmg80/VvjwY3dHV3dnV2Bjm5/Z3egyx/sDIS6gwsvvO3Gku+YxwJASpA1AQBgOHK77lw8f+mdLy363du/PuH//GdvLnqt5c/dHaGuk53dJzv9Hd2Bdn+gwx/sCNx84R3LrqmxDgOAFCFrAgDAMEXi5qp/9jS2vHH5H4re3L2hu5P1dyiB9m4SNLtPdZGFs9hxtTc8f8/Vj3Is/rkEGCT8Pw8AAAxfcwovfen2psf/4b9vmnHLNNsMhzraHnYUitMrpn675rrnXqvc+c3zbrSKAsCgoB86AJwZ0A8dAOBMhHZNAAAAAMgWZE0AAAAAyBZkTQAAAADIFmRNAAAAAMgWZE0AAAAAyBZkTQAAAADIFmRNAAAAAMiW3B1fEwAAAADOdGjXBAAAAIBsQdYEAAAAgGxB1gQAAACAbEHWBAAAAIBsQdYEAAAAgGxB1gQAAACAbEHWBAAAAIBsQdYEAAAAgGxB1gQAAACAbEHWBAAAAIBsQdYEAAAAgGzB+9ABAGBYUz9e3XFsO6urLMey9A9HkFlk4iMbeYas8k72nGrrSABIAto1AQBgWOOlvIKJLk7Kp40vtP3F+GEu90zGDzpT/dZhAJAcZE0AABjmdE6w5Y2fL9jGmNHSCpdWxNTInE7GqjEBQAqQNQEAIHc1NzcfOnTIWskWkiODLKs7J5SJzkIjZJqZU9c1Y06XaTFjAVkTIDXImgAAkKNI0Ny5c6fX6z148KC1KSuMBKn5GTVgP/tieXQpjZVWE6YZLsmkRRbIlHGeSpao9Firw1BWvwF8vV8xZE0AAMhFu3btIkGTLOi6/s477xw4cMDcngWRBKl1MeopKX+a2X4ZPydljBXjEABIFrImAADkovz8fJZlzWWS8bZs2bJ//35zNcN606POaCFGV41sScOllSzpspEzjRVjDsOAz1NbWVZW67NWYbCQNQEAIBcVFha6XK7ouLl169Z9+/aZqxkViY+6HlKDgXDACJlG3CR7rAW6RCcrccLQ53t1RXW911qBNCBrAgBAjiJxs6ysjOOsf6pI5mtqasp43AwowXUfvnV7w8Nz1/y45OnbS5/69uUbn77Du/4539/aSe6MREwjYyJoAqQMWRMAAHLX5MmT4+Lm1q1b9+7da66m75XmtVetvXP51lVbTnwYtIUdI2XHSJvq1HYGj9X4vNdufGZN63azkZO2aFqtmwCQAmRNAADIaZMmTYqOm0RTU5PPl+5TdJquLW9YvKThtg6+214gOQpkR75kp5Nsd4r2PMGeL6h27fd7Nld5Xw6qYatp02zfHAyfp7KsjHaINpSV1SbqF208ImgVIWUqPX0/pvkUoVWEFKqsjStkdLs2HjNMojYiplT/xQbmi/94ldHPOZ72mvvTf7W+Wro5QefyJHudD3RJZt3F1fQGure62CoR8+Bm/OEJPlLvFdIPYRTruaqk/mMYMpA1AQAg15G4eemll0bHzXfffTfNuPngxjvX7X5azrfZ8iQykZRpy5dp4iyQbfmCPU+U8wSbU5DyeGH0hb96d6ORMQcdNEkAKq6o93pdbsrlYrzedS3x199SW1ZcUV3PmGVIzPHWVxTHRRyziJfprai+uqI4UbJqjamNnrFPbQTJPDEV9lNsYLQS4+NF17LuVauOFK451sDVpuN0lzRjoXk+yizidi+cYawSNEQah1t/neaFkeNJTLeKRPNU0g9hrVBJ/ccwpNAbAwAAALnt8OHDa9eufT5KS0uLtS91G/e8dN5v7aX/OWrOqgmXrj37qvUTrmmYVPFa4fVvTL3+zenXvnrWN14ec+WfR136XyNWfbDyiW2/veB34pOvfDPsu1vdd49VRSpaa2hscdW0WutUa0Nrz2qD2/wnObaIeRTDuBusDURrjdvdEF1Ngrp7aostaVUXXTLBpkQVDixSSdzZGmoi68lfc+wnHbhac3/0EZa+VSWq/PSXlGgTZX298cf3bLY29BxPsuRpq43+j2EIQrsmAADkOhI0N2/erGmatc4wc+bMKS4utlZSpGjKb974heiQJIcsO22yXZQdos0p2fMkO53LNqdgcwiyXbhlVtWCGbc+/+Fjoo1/fE9je9BPb6EHUh7ps3U3bdYqmVFkrhqKyouiVyl3Q2NV1LaiqtVGLGmOavMqqqqrK485rui6haSQd3ertd7D3RBb0qrOW70y0vjmWUnvEsedlZRbSmJTVLGB9VQSd7byqsh6Ctfc6/TVpmNQl2Tw1a6oJz/iv1xyYXVGiKxfEdck7PWWxH7BSf7HMJQgawIAQE4jQbOxsTEuaBal8W/zppYNR7vbRFkUbRKZJDuJlaLNJtodksMpORyyzSaQ6euTb7hj5vJVe1YG2U5e5gKs8uKBnQyja8fXMqFPrLqSUzyTpBASQwZ+EtK9oNxaiiiaUWItxfJ5PLW1lZWVZYT1WGEffWuz8lRPdvWsJ6nJVbOkT7nyBbSNLjri9s+oJNHJ4iVzzb2SrjYdqV2SyUiKib60nrgad5O/T9nk/mMYUpA1AQAgd7W1tUUHTZZl586dm07QJN5s2cCJPC8KgiiIkihKgkwmG42bDrtEJpssFI0uvXPm48cCba8ffU4UOUHkeJH96xEjHWgB/fgao6ZkFVWtbqAP9dVXFBvdSAafMsxeJsUVFdXV9fXNZENJ5LHCOK6ZCVp9Y7Krr4Ue39P1JVoFDXpJMSsZOBMmfc29kqk2HYO4JJN5YbGNkj36+58GsTL3H8MZA1kTAAByFAmaXq83Lmiee+655uqg7Tr2Pi/wkYkETl6UeEniZVm0y5LdJp3lnPDL4lUiJ9f77ta5MCdwHMdyPLv75DGrCi1gLSSrqLyukT7TF+mjww4qY/hqF9FeJi7rmUWSwhvr6pYYjZWDFun60kdvX5i0ZOOa0/SlXlKCXJqZ/xjOIMiaAACQiw4dOkQyQHTQnDdv3vTp083VdHzedZzlWDrxJEQaOZLjeJ6TBE4WxQK5YHHhUyOFsU0nNu449ZaxlwZNMu9WQ34lbPRGH1SHdJoxGo1uJEaz1qLUOnuTjPTqOi9jPF8Z/ayg+fxfUmJa5cxWONfCJXWJpfBg5AC329O55uTu4qcs7a+xvwsbsNEzXrr/MZxJkDUBACAXtbe365HBLEnQnD9//rRp08zVtFnvvYyg2ZFu0hmeE24esbxQviCgdj17eJm5OzpZ0n81B5Uzo5CUYfbSOW1HlDgJ45AZcPqIf2yQMvvb9NxdNx4cTFQuBdYziv13JErlmnudttqI+vXxJcwnPQcyuEsyDXRhZoZN+PBC/wb9H8OZBFkTAAByUYmBLJhBc+rUqeb29I3Ln6hr+jjHxPNGlGiUrqmaoqqayn+rYNl54lyd0VYduecL/8ekGNmraoym0oUC0SbzAq0itbjp83ji7pGm0ojWy+pVEhWvfJ7KRf30afFWL4q5M0uKGo9hupdGOkVHOpwX97mDS8rGDMU+ALMSpr4ibkhOn6fW2JDSNfc6XbWR1BfX7zvyIQeU2iXFhcD+L6zS6F3U+/X2I1P/MZxJkDUBACBHzZo1i8TNSy65JINBkyidMNvOOR65fPUPZv6SpMywooRDKquIt4196CLbFaTA1hN/aTz+gqJo5qQrZh7VS0aNN2tIVcsKqx+IoczsfEOHzbH2JykSryqsquiI4vUlNcbASPFcNTUlxqOAVklalG5tjT5pZJyeqKsz3mZDyibVymewKjE6GUUqoVWsM/amcs0xBq6WsFIfLWBVTeturqmhWweS7CX1lDOK9byxqL8L6/v1Jpah/xjOKPSxWAAAgGHj7b2vX1wz7njnUbL8R19dxWuFd2y9dG/nTnPvMX/bbe/O+vbbE/9h0/hrPWdf+cJo15qCv/+DY+bD0h895Qodzn2ZduC+cDhME2hyzH4gPVx0gPLosbv7DjZuMXbEjgROn++zuNx0j5F8og/uOai1oSamcOzo4xFGqdir66fkAFpjP2LsJ0z2mvt+AwNWS8R+QnPc975VJag8iUsyRBWLGby974X1/c4S12icuvfIvp9pCGLJH+vjAgAADAPkH74bn5wz45ySh8ufIasHOvaMc0x2CHlkOagFlmy/9lDnR6qiKWEtHNKCfjXoVwKdal5Yeq3C7RBlo6MQf7JgcV5eniAILBv39OdXzlPJVtSTrBk7RDvAVwVZEwAAhp2mg29//0/l91f8x7dLvm9tYhhVU+774Nb3Oz7UNV0hQuFwIBT0B4KddPrtNU/cWPIdq2hOy2DW9Pk8rf33WSkuHtqvu4EMQdYEAIDhqN778L9vWfG765++tvgfWZYlQfOBpn97+5NXGHE0+ZdRC6vhcFgJhkPdQTL90wW33nvtY9aRuS6DWdOoylruy92gD+mnDCFD0DcIAACGI7frzsXzl9750qLfvf3rE/7Pf/bmotda/tzdEeo62dl9stPf0R1o9wc6/MGOwM0X3rHsmhrrsOGlvI7k7n4haEJSkDUBAGCYInFz1T97GlveuPwPRW/u3tDdyfo7lEB7Nwma3ae6yMJZ7LjaG56/5+pHORb/XAIMEu6hAwDAsKYzeuO+Nzd+tH7H0W3Hu47qunaWY9zMcRd9Y8YNVxZfJ3DGgJpnEvQNgtyCrAkAAAAA2YKbAgAAAACQLciaAAAAAJAtyJoAAAAAkC3ImgAAAACQLciaAAAAAJAtyJoAAAAAkC3ImgAAAACQLciaAAAAAJAtyJoAAAAAkC3ImgAAAACQLciaAAAAAJAtyJoAAAAAkC3ImgAAAACQLciaAAAAAJAtyJoAAAAAkC3ImgAAAACQLciaAAAAAJAtyJoAAAAAkC3ImgAAAACQLayu69YiAADA8KN+vLrj2HZWV1mOZekfjiCzyMRHNvIMWeWd7DnV1pEAkAS0awIAwLDGS3kFE12clE8bX2j7i/HDXO6ZjB90pvqtwwAgOciaAAAwzOmcYMsbP1+wjTGjpRUurYipkTmdjFVjAoAUIGsCAEDuam5uPnTokLWSLSRHBllWd04oE52FRsg0M6eua8acLtNixgKyJkBqkDUBACBHkaC5c+dOr9d78OBBa1NWGAlS8zNqwH72xfLoUhorrSZMM1ySSYsskCnjPJUsy5bV+qzVhIwybKXHWgU4YyBrAgBALtq1axcJmmRB1/V33nnnwIED5vYsiCRIrYtRT0n508z2y/g5KWOsGIcMPZ7KstMHXoBBQNYEAIBclJ+fT7KPuUwy3pYtW/bv32+uZlhvetQZLcToqpEtabi0kiVdNnKmsWLMASBZyJoAAJCLCgsLXS5XdNzcunXrvn37zNWMisRHXQ+pwUA4YIRMI26SPdYCXaKTlTiHnvK6RvJBG6uKrHWATEHWBACAHEXiZllZGcdZ/1SRKNTU1JTxuBlQgus+fOv2hofnrvlxydO3lz717cs3Pn2Hd/1zvr+1k9wZiZhGxhyqQRMgi5A1AQAgd02ePDkubm7dunXv3r3mavpeaV571do7l29dteXEh0Fb2DFSdoy0qU5tZ/BYjc977cZn1rRuNxs5aYum1boJAClA1gQAgJw2adKk6LhJNDU1+Xzp9mHRdG15w+IlDbd18N32AslRIDvyJTudZLtTtOcJ9nxBtWu/37O5yvtyUA1bTZtm++Zg+DyVZbT7jamsrHbALuW+WrPswB3PfZ7amEoraz2x30tvD3ejqFmMnr0yriD6uUPWIGsCAECuI3Hz0ksvjY6b7777bppx88GNd67b/bScb7PlSWQiKdOWL9PEWSDb8gV7nijnCTanIOXxwugLf/XuRiNjDjpokiRXXFHv9brclMvFeL3rWvq9fhJLi6u9DONuaK0rt7b1ReJocUV1vZfprbS+uqI4UVxsNYsyPQW99RXF6HMOXw5kTQAAOCNZj1AOyusfvfzHv/1Bcsiyw2ajrZgSmRxOyZEn08khyE7BTuYO4aeXPvD3k+dtOv7Rmo+2GVFzMCf11a6oZxhXTaveWEc1Nup669LrrL3xSNCsIMWNoDlwT50SUkbXG3sqba1xMUz9ivgM6a2uqC4xSsYU9FYvQtqELwGyJgAA5LrDhw9v3rxZ0zRrnWHmzJlTXFxsraRI0ZTfvPEL0SHRrOm0yXZRdog2J42bdjqXbU7BRoKmXbhlVtWCGbc+/+Fjoo1/fE9je9BP820g5ZE+W3d7ybxkRnRyLCovShQkfbVlNGiSXHq6oMkUVdXFlSm6biHNkLtbrfUe7obYkkVVq820uRL3zCHrkDUBACCnkaDZ2NgYFzSLEga15Gxq2XC0u02URdEmkUmyk1gp2myi3WE0bTpkm00g09cn33DHzOWr9qwMsp28zAVY5cUDOxlG146vZUKfWHUlp3gmCXZM/Yo+D0nGoXfFq700aCY/9pDP46mtraysLCNY4857X+4Ffe7Em7GUae7/Rj5AhiBrAgBA7mpra4sOmizLzp07N52gSbzZsoETeV4UBFEQJVGUBJlMNho3HXaJTDZZKBpdeufMx48F2l4/+pwocoLI8SL71yNGMNMC+vE1Rk3JKqpa3eCmj1NWFCfsl2PwrluUWtCknY1Ytriiorq6vr6ZbChxk5Mk4JqZoAW4aEaJtQSQXciaAACQo0jQ9Hq9cUHz3HPPNVcHbdex93mBj0wkcPKixEsSL8uiXZbsNuks54RfFq8SObned7fOhTmB4ziW49ndJ49ZVWgBayFZRXSo9FYSOK1+OWzfwOkqKaFJ0bsuufvavtpFFbRfUA19EJM+tEkf21xiNFYC5BRkTQAAyEWHDh2Ka9GcN2/e9OnTzdV0fN51nOVYOvEkRBo5kuN4npMEThbFArlgceFTI4WxTSc27jj1lrGXBk0y71ZDfiVsdA8aVLckGjhpryCrjTO+Z87MJY20zw7Zk0QPcd+r64yO6o1V0Q9img+GJsXXYjSFxjxDCpANyJoAAJCL2tvbe3qak6A5f/78adOmmatps957GUGzI92kMzwn3DxieaF8QUDtevbwMnN3dLKk/2oOKmdGIZHT7JnTtw9PUVVjg5v22TntgEQJU6WZH/vwrnu1T2WelfTRzoR31wEyC1kTAAByUYmBLJhBc+rUqeb29I3Ln6hr+jjHxPNGlGiUrqmaoqqayn+rYNl54lyd0VYduecL/8ekGNmraoym0oUC0SbzAq0itbjp83jibpgP0P5YXteaTNy0uhut773f7vNULkrcNYiObhRzy54UpZ3dGfdSvP4csg9ZEwAActSsWbNI3LzkkksyGDSJ0gmz7ZzjkctX/2DmL0nKDCtKOKSyinjb2Icusl1BCmw98ZfG4y8oimZOumLmUb1k1HizhlS1rLA6BRnKWNYcP7OfcdqLSNw0RyQaIG5a/cjrK6xqy+hY8SU19Lg+XDU1JcYzolZJWtQaVskqAZBFyJoAAJC7SNycMmWKtZIhV5+/QA2rBfLI0jFzvjf9R0pQK2DH3jVtVanzMrL3eODwE3vvCgTCoaAaCqnhkEYmJaSpYf0bE3vvOCuKkvRg8kXXLbU6BRm8Lpe7oXXAoFdUZTy6OVDcpEXog59WtYy7plWv6294+OvqWhtq3IxRkt46d9c0pDCsEkBa2HTeuwAAAHDGIf/w3fjknBnnlDxc/gxZPdCxZ5xjskPII8tBLbBk+7WHOj9SFU0J05QZ9KtBvxLoVPPC0msVbocoGx2F+JMFi/Py8gRBYNm4pz9ziqeSrahPabxOgExDuyYAAAwvJB3ec/XvPc3/8+ddq8nytIILzKCpasqDH9yxv7tTYSaGmfFh/eywOipMQmbIpoT4u67+Q/75K/lzV3DT7mOnLBs1apQoirkdNAFyArImAAAMO3OnXFZ96b33baz2tLxg3t8jQfOBpn9rPLixu6Oru7Mr0NHt7+wOdPmDnYFQd3DhhbfdWPId81gASAmyJgAADEdu152L5y+986VFv3v71yf8n//szUWvtfy5uyPUdbKz+2Snv6M70O4PdPiDHYGbL7xj2TU11mEAkCJkTQAAGKZI3Fz1z57Gljcu/0PRm7s3dHey/g4l0N5Ngmb3qS6ycBY7rvaG5++5+lGOxT+X/3979x7bRn0AcNyPO5/tPOiAtVD6oCUJW0krEOsDp2wDMUZTgZCGqvHHxkOQbNpQgkQHaEDL2mlDsC2ZJrSEAWVlE+sfrGKQbGEwttWmSREwmj7jljYtdI20AU2T+HV3+/3uLn2EpK2d/FSzfD+6OvfzOT+3kap8db6fDRSItUEAgEnN9tnxva917Nr43qG3+gYO2bZ1fnTavGmXX1d14zWVy7WA84aan1WsDcLZR2sCAABAFV4UAAAAgCq0JgAAAFShNQEAAKAKrQkAAABVaE0AAACoQmsCAABAFVoTAAAAqtCaAAAAUIXWBAAAgCq0JgAAAFShNQEAAKAKrQkAAABVaE0AAACoQmsCAABAFVoTAAAAqtCaAAAAUIXWBAAAgCq0JgAAAFShNQEAAKCK37ZtbxcAgMnH/PC5/sNv+23TH/D75Z+AIG6Gt+DwnUGfGAZL/Bc1et8J4AxwXhMAMKkFQ6Xl02OBUJk8+SLPvzhf3P1jm/NF3phD3rcBODO0JgBgkrMDWrj0giVa+Dw3Lb249BLTErdyc4bOBiAPtCYAoHh1d3f39vZ6A1VER6b9frvkwhq9ZJYTmW5z2rbl3Mp9+TBnh9YE8kNrAgCKlAjNrVu3JhKJ/fv3e3cp4RSkNeQzU5HPX2mcu0BmpXcK041LsVnDO2KD0F7vF+rbvSEwJloTAFCMtm3bJkJT7Ni2/eabb+7bt8+9X4HhgrQGfOYnobI57vnLkbfiMc7A+Zb/J+31NaIaa5qT3hiYWLQmAKAYlZWViQJy90Xjbd68+f3333eHE+x4Pdo+K+OzTactZVx6ZSn3nc50Bs5t/pLtzfU14wq68c8AnA20JgCgGM2aNSsWi52Ym52dnXv37nWHE2o4H207Y6ZT2ZQTmU5uiiPejtyTm1eceUu+sraxNeENCjL+GcawrCUu/onxhgpvDEwsWhMAUKREbtbU1AQC3q8qEURdXV0TnpupXHrDjjfuanti0frvVz9z14Knb/lKxzN3JzY+n3z3iOjO4cR0GrPA0AQmM1oTAFC8Zs6cOSI3Ozs79+zZ4w7H70/dL1z7wn2rOtdt/mhHOpyNTjGiU8JmibU1fbgpmbih49n1PW+7JznlGU3v7CaAPNCaAICiNmPGjBNzU+jq6komx3vZomVbq9ruWdl2Z39wMFIeipYb0bJQRG5GpESPlGqRMs2MWL/Yuakh8VLazHqnNt3zm/lINsulN5WN8uXvRGOl2Be8yy7dY6Ms5j5plfepZvC413K6R4Samvrm9hE/oePPlXRXAw3PP+qK8pETfno+4AzRmgCAYidyc+nSpSfm5pYtW8aZmz/uuG/D9meMsnC4NCQ2UZnhMkMWZ7kRLtMipbpRqoVLtFBpUDv3sge3dDiNWdBJzaoVdULM2Y/JXWFFlTM8Q6eeQUZkZW1jayLhHYv5EonWxtrKmlEiVqZlZe1prvsUMzoTek8WExPK+UadDjgdWhMA8JnkXUJZkFd3vfS7d38dihpGNByWZzFDYouWhKKlhtyimlGiRcRtVLt36ZorZi5+vW/X+l1vOamZ95NWLGtoaWlZuUKmYmzFSrEvNCzLYyXOKWcQ6ShPeNa19dh23D0Ut+2etjqfCMTaT+Vh99q1rbEm8VipZZl376dUu/O5E8bjdk+TePLWtSyCRwFoTQBAsTt48OCmTZssy/LGPt/ChQsrKyu9QZ5yVu4nf/2BHg3J1iwJGxHdiOrhEpmbEXlrhEu0sAjNiPat+Q03V93x+x2/1MPBJ3fGj6SHZN+m1L3TZ96SzWtbxZe6tpaT47ViWcvoeZhIVLeddsl5hWjbEfMtl6Wb2N7jjYEzR2sCAIqaCM14PD4iNCsq8jgvOMLru18+NHhAN3Q9HBJbKCKyUg+H9UjUObUZNcJhTWxfnnnj3fNWrdv5eNp/NGgEUv7cH/dt9flsq+8FX+bf3lxnW8/2hM8Xa1o5yglKLw83vHJSbI7+2FEl29ubm+vr62sE72pRoAC0JgCgeB04cODE0PT7/YsWLRpPaAqv7X45oAeDuqbpmh7S9ZBmiC0sczMaCYktbGgV5y64b96Th1MHXj30vK4HND0Q1P1/+8DJNitl9613Zjrrkru7xW111ag/j4qqam8vX+7iocra2sbG1lbnGYavFgXyR2sCAIqUCM1EIjEiNC+55BJ3WLBth98JasHhTQRnUA8FQ6GgYegRIxQJh84vufD+ynV6wGhN/tAOZANaIBDwB4L+7R8f9qawUt7OZ84YXXqiZPNtcvFQrElesikv2pSXbbpXiwIFoDUBAMWot7d3xBnNxYsXz5071x2Ox38G+vwBv9yCIiKdjgwEgsFASAsYul5ulN8z6+kp2tSujzre++QN56gMTXE7aGaGcllneVDhy5IU6N496pKdU570HFvylQ3OWqP4SeuXnBfrgULQmgCAYnTkyBF7eKW5CM0lS5bMmTPHHY6b97mXw2Q7yrtsXzCgffOcVbOML6bMgd8efMQ9fGJZyt+aE96ZrRtHrhZv3yhX/Jyed01m4+OjvBuR24yxefmuoBq1Kt1uBQpBawIAilG1Q+y4oXnxxRe794/ftLLptmVPi06/9JxqS7It08qZpmUGv1H+yKX6Ittnrfvg4f8OfSgeJo6als8y5U65HjaCmpyi0NwcuZDbbcURq8WT7fW1Y6bmiBkqGh6qE19aa09+a3c5h/tWSA/l/THnlfOcv9IJ/Ssmu42lQSgYrQkAKFLz588XuXnVVVdNYGgKCy78UiQQ/dlXnvvuvPtFZWZzuWzG9Of0O6c+dnn4q+IBnR/9Od73Yi5nuZudc3vUrv7cBe4MBRiuytqaGrm0u95LQ68V5acBOQfkZ/VU1nY3Ncl7TzbGDN6bGzmfJ+RO4a7rEbUaa+oZ+x00x3TseYb/RnKy6ib5JEAhaE0AQPESuTl79mxvMEGu/8LNZtYsN6YsOG/ht+d+L5e2yv1TH5izbkHJ1eJoX+rgU3seSKWymbSZyZjZjCW2XMYys/Z104+/Hp3L5Y69xH9GKhribXIxdyLR2tqa8B371B/Rim1Nxw6IQqxr64k3jPaxQmPNIO7v6RFHnI8LkhKxWJ1c2HPa99EcnZzv+BO1+uqaeuyW5d5BIG/+/P6rAADwGSd+8d30m4VVF1U/sexZMdzXv3NadGZUKxX7aSu18u0beo/uMnNWLisrMz1kpodyqaNmaTb0l9q6qG44C4WCH5ffU1paqmma3z/i6k8AJ6E1AQCTTtf+f97+h2U/qv3VLdW3e3f5fKaVe/Rfd7zTv8O27JyQyWZTmfRQKn1Ubj/9+lM3Vd/qPRTAGeM1dADApLNo9tWNS1c/2tHYvvtF95yLCM01Xd+J7+8Y7B8YPDqQ6h8cOjqYGhgSlZkZTK+47E5CEyhMcPXq1d4uAACTxpUzY5qtrXm18ajZf9nUKx78e/0/9r+UzZiZtC+bymTT2cxgJjOUzgykb51f9/D1P+e1cqAwvIYOAJi8tvRuWtNx777+pBWwgnrQ78v6/CHbZ1s5y8qaF0RmPHDtY1+79Cbv0QDyR2sCACY1UZbxva917Nr43qG3+gYO2bZ1fnTavGmXX1d14zWVy7WA84aaAApFawIAAEAV1gYBAABAFVoTAAAAqtCaAAAAUIXWBAAAgCq0JgAAAFShNQEAAKAKrQkAAABVaE0AAACoQmsCAABAFVoTAAAAqtCaAAAAUIXWBAAAgCq0JgAAAFShNQEAAKAKrQkAAABVaE0AAACoQmsCAABAFVoTAAAAqtCaAAAAUIXWBAAAgCq0JgAAAFShNQEAAKAKrQkAAABVaE0AAACoQmsCAABAFVoTAAAAqtCaAAAAUIXWBAAAgCq0JgAAAFShNQEAAKAKrQkAAABVaE0AAACoQmsCAABAFVoTAAAAqtCaAAAAUIXWBAAAgCq0JgAAAFShNQEAAKAKrQkAAABVaE0AAACoQmsCAABAFVoTAAAAqtCaAAAAUIXWBAAAgCq0JgAAAFShNQEAAKCGz/c/ToZlU2A8RQwAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": { - "image/png": { - "width": 700 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image('images/folders2.png', width=700)" - ] - }, - { - "cell_type": "markdown", - "id": "c86ad72e", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Opset, Options\n", - "\n", - "**One model, multiple ONNX graphs**\n", - "\n", - "* Every opset (ONNX version) may change operator signature and introduce new operators.\n", - "* Three ways to output probabilities\n", - " * A single tensor ``[N, C]``\n", - " * A list of `N` dictionaries ``{class: probability}`` *(default, historical reasons)*\n", - " * A dictionary ``{class: tensors}`` (requested by a user)\n", - "\n", - "```\n", - "to_onnx(model,\n", - " initial_types=...,\n", - " target_opset=14, # select opset 14 because the \n", - " # runtime does not support higher opset\n", - " options={'zipmap': False}) # select single tensor for probabilities\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "e140addd", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Option for a specific step in the pipeline**\n", - "\n", - "What if an option only applies on one step, one model, one transformer...\n", - "\n", - "```\n", - "pipeline = make_pieline(....)\n", - "\n", - "to_onnx(model,\n", - " initial_types=...,\n", - " target_opset=14, # select opset 14 because the runtime \n", - " # does not support higher opset\n", - " options={id(pipeline.steps_[0][1]): {'split': 10}}) # select single tensor for probabilities\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "6e21d363", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Why options? option `optim='cdist'`\n", - "\n", - "* Operator CDist is not an ONNX operator\n", - "* It can be implemented by operator [Scan](https://github.com/onnx/onnx/blob/master/docs/Operators.md#Scan)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e02c1fcf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": { - "image/png": { - "width": 250 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"images/cdist.png\", width=250)" - ] - }, - { - "cell_type": "markdown", - "id": "5093ff21", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**CDist vs Scan**\n", - "\n", - "CDist implemented in domain [com.microsoft](https://github.com/microsoft/onnxruntime/blob/master/docs/OperatorKernels.md): much more efficient." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "26ed85de", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy\n", - "from sklearn.datasets import load_iris\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.gaussian_process import GaussianProcessRegressor\n", - "from sklearn.gaussian_process.kernels import ExpSineSquared\n", - "from skl2onnx import to_onnx\n", - "from onnxruntime import InferenceSession\n", - "\n", - "\n", - "iris = load_iris()\n", - "X, y = iris.data, iris.target\n", - "X_train, X_test, y_train, __ = train_test_split(X, y, random_state=12)\n", - "clr = GaussianProcessRegressor(ExpSineSquared(), alpha=20.)\n", - "clr.fit(X_train, y_train)\n", - "\n", - "model_onnx = to_onnx(clr, X_train)\n", - "model_onnx_cdist = to_onnx(clr, X_train, options={id(clr): {'optim': 'cdist'}})\n", - "\n", - "sess = InferenceSession(model_onnx.SerializeToString())\n", - "sess_cdist = InferenceSession(model_onnx_cdist.SerializeToString())" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "c2550697", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.67 ms ± 49.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" - ] - } - ], - "source": [ - "%timeit sess.run(None, {'X': X})" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "0a1b6f44", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "466 µs ± 11.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n" - ] - } - ], - "source": [ - "\n", - "%timeit sess_cdist.run(None, {'X': X})" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "f96b164e", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "%onnxview model_onnx_cdist --size=\"8%\"" - ] - }, - { - "cell_type": "markdown", - "id": "ad906fe7", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Main source of discrepancies: float instead of double\n", - "\n", - "* Every ONNX operator supports float, but not double\n", - "* Big miss: TreeEnsembleOperator\n", - "* Huge discrepancies because stepwise function are not continuous (see [Issues when switching to float](http://onnx.ai/sklearn-onnx/auto_tutorial/plot_ebegin_float_double.html)).\n", - "\n", - "Lightgbm compares double, onnxruntime compares float." - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "601eee1c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 25, - "metadata": { - "image/png": { - "width": 400 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image('images/double.png', width=400)" - ] - }, - { - "cell_type": "markdown", - "id": "69480862", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Small discrepancies = distinct decision path**" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "04a5b76e", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": { - "image/png": { - "width": 500 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"images/stepwise.png\", width=500)" - ] - }, - { - "cell_type": "markdown", - "id": "2946adfe", - "metadata": {}, - "source": [ - "**Solution:** compare onnx predictions with lightgbm predictions on **float features** only" - ] - }, - { - "cell_type": "markdown", - "id": "246b31cb", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Small discrepencies = random walk for huge forest**\n", - "\n", - "Huge forest = 50.000 trees" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "80337bcf", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import pandas\n", - "rnd = numpy.abs(numpy.random.rand(100000)) - 0.5\n", - "c64, c32 = numpy.cumsum(rnd), numpy.cumsum(rnd.astype(numpy.float32))\n", - "diff = c64 - c32.astype(numpy.float64)\n", - "dfn = pandas.DataFrame(diff)\n", - "dfn.columns = [\"cumsum\"]\n", - "dfn.plot(logx=False, logy=False, title=\"Random Walk - uniform variable\", figsize=(14, 4));" - ] - }, - { - "cell_type": "markdown", - "id": "9027994f", - "metadata": {}, - "source": [ - "**Solution:** split the operator [TreeEnsembleRegressor](https://github.com/onnx/onnx/blob/master/docs/Operators-ml.md#ai.onnx.ml.TreeEnsembleRegressor)" - ] - }, - { - "cell_type": "markdown", - "id": "13ec77e5", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Split the operator TreeEnsembleRegressor**, then cast the output in double and do the summation with double precision\n", - "\n", - "Regression with 40.000, 200 features, trained with [lightgbm](https://lightgbm.readthedocs.io/en/latest/)." - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "0c3a7ecc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('images/treesplit.png')" - ] - }, - { - "cell_type": "markdown", - "id": "689ba2df", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Sparse Story\n", - "\n", - "ONNX: Incomplete sparse support\n", - "\n", - "* explicit use of Sparse\n", - " * [SparsePCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.SparsePCA.html#sklearn.decomposition.SparsePCA)\n", - " * [sparse_coef_](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet.sparse_coef_)\n", - "* Dense to Sparse\n", - " * [OneHotEncoder(sparse=True)](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)\n", - " * [TfIdfVectorizer](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html#sklearn.feature_extraction.text.TfidfVectorizer)\n", - " \n", - "Once it is sparse, everything which follows must handle sparse as inputs:\n", - " * linear models\n", - " * ensemble\n", - "\n", - "[Combine predictors using stacking](https://scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "b1b54e87", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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7Q8l1xJ7ZtdmjI/6WMDoQORkQ71oAWLYS/v0me/aeUFW7dTg4k60zZ5Px6QnvkJU9IZNJXeg3Zap9jsboCGYfZH0zNCxcvhG2H/GU+mikBUFj5UQfrbYy1SW9kGSG70b63i+wgeHJzE/TbB4Y46plcY89cTrABxvxE9vFPeoCpxgPy/bWFNSx6hbrwRFfeDqeZBU5MXt7yvOcENylAVcQmeRaBOutIxGuy5oUjW9OeoJXWJIT873qysYQ/UBgRljZ3WQ8GvKdshlXScOn5N7eFss59jKJb5Y17FUef+sx2aiK22Cltz0oReUhHX7Flj0oMs9qn6GNltnklJe/vils3+eSjYPnB+1vkgCdPTqSU0Nb2Tsqlds3352tALAcpCMvWyzkSKc0xgF/tqWdqS1mpvwjfTukFeZy6X4q624wpcaaqnQ/FdSx6pY9fSP+kDR8iitzVfi4XKdGQfczfdHTn41xmt0jU6xlRGR6n1MBj2NPbu9TmLVjxYqOI+xVwq5nL/LEk2JV3gZT3L9TVB5uefv53nG7zRNhuT4bm3BkL5ROvGkZPC/b+1QY3KGBlDqKyxU5ZcY5bAGWjZkRoeovcredYDY4wNco+eejZt9h3YQM2Yt8pWcXF+Za4OwZufwzusKf5b8ovVZImg/JMRt7Xe78WPEwnEt6hlC6rmsjBvZ60dwTmXld+Ibk/Kf4dDfrsPGmhtg4OFm/Q3IOV7xftufu3IKKnPDMJfyi2YkXhUKUV+pmJw4KhUgyLpBzUlpy5y/5FnuZoFkh2rbsKdD8YiP8TeE8s7iQm97I2bykbw97S/yVDGmeS4+OmZEt7PXcb/FwS04dURQQ/5DDFmD5EFcU0upIjjjg5l8OKQnH8teziCOj/NUxBUMkUyi2imtXdjuF6GMbHTSECJWqcBWVOALm5sPUSf7R3IUvz5xnFDhHhXVsjtmJPvb5nIArzh+yGsltH8m3hRaB9G4VcT4MzNsSyMiJaEXx5aHsNpiytkfZ5YE/9U7i7mFxwyNj6rioPEhKuDTPpUcH+VHsZaLw5XuYEaKuKIqJiqIt1ApRRZFTx8qpTvdTHEQW5FpgRd2N8mINT1H3s8I6Noe4z1WsK1Ri91PSEsjtdhUkbXgUJWpalNsGU9j2KLc8iHvHptyD4rbPyt6qLLjnQ/ezjlQeEMt5OtyvctgCLBfXg95xllRtdTh2yc6Tk5UOjQ9mT1lpevebc76g3tLdy9dg50JTcierFpzVvofvbDB9nzWJegmy9Ef9bnE+NK9uYSny66t8ejl3Xeqm0ua0FzStmv8bJFx5DutFJ1FbWjawlCqhkvyklWrhY+f7el8OREXP0KuadCjwslCIenNLndpgEgqR/7JMISKtpdEB/Sq2lMm7B1mqgD12K9/yYPps22WLQ+Sb7uz22azbcjavuWML31jxhyIyRbzd4X9NfHQ0zb9rN/Z53u4zric/rdV8KjC0TWlZgBqiKCA+w2ELsGxIouHAS32i6kiGOODu6LW251zopO3azdctCV94qa7a0A0dMOXUPob9ZnEIkZVbLataNHwtmFBV68F3VBl1bA5102qWKkLvOO82r2ULRPPDohaB5BepVUJIGew74A7fWojr0arTBlPS9lBWHhLvBgIsqbOYcj+le7KbpYqVcNKJ9QyIjg71g/PuqGbj8cDwrlby07Vb+8ZG+3Lbf7CsKIqJiqIt1IjYJW+2olAZX3KI61gZy6T7qbC7IVJ6rKlcGXVsjqJ9rixF3U+WID2rQ72D40UfRV62qrTBFLU9lJWHSPhstozvsxpzDoqHOgz8OH51gnsWup91pPKAWM4oMCxHiUiQf6ymYbshJ6YWEo1cZCmVyqLPrdqIVt12llKpQtEle1JzgUdsr34wp7ovrGtLSb3dqshdl/hOn7fyJga6n4qOu+37u8XPaRU/YF1Ox9ZS9ixH09ElnCtOBD7f1fZw5umiztFARKZDK763d55z7PwvuhoRHua6W1+gt/aoju8pqsLRwnflPNllkAZI8ZYM5EwvpW3Ji2mrm2R3c2z6cjYMq1xdq1lW88R5Hr8n0883GqRDQhrzW2zbitylpd02NHZtbi4+7XlOt2iFEADKEg2/yVIkGhpKiBupqFD1GbYUmARQ09rB14eRa/El6sYWiJ7Nq5pYqri8ankBKaxj07fC3lf7e55iD/XmiB+wLqfLkNtRlNNq2C9Es/CrPR1rmtbpe/rP+sNyDzeX3NsrvlCrwLXM2aBWnTaYkraHovKQno7w8THSr2MZLRBNGxWRi56qjq4npW2W9gG2bYTcJCGrdDbvdHxubiYwlNt5BoDakohcFoKhKed4L2yZdD+VdTfESo81lVNYx6YT4VFX/97OzsdF4VM0CYOc0rufGn2XcDFwImA3tq1e09a13+kdjyRkep+iLZZcDJt/H5VvF4tp1WmDKWp7KCoP16eF+QxPF+h9iqaNiqdk5ktUNrCQhe4n8GRHgb/IYQuw/MVvRVlKpdKtFc46ykunhYnwClZk4ktnI9W+drZxxc73d65Z3WbsGTztD04u3IyRGvNrE9Ih1ET0gqvf3LVpTVPL45bB89EqDEyk08IJ1gfnK0RXFr8QJRJCWxMAoJBUPM6317e0aktoOKfvxVlKbhztoRahy3QnhfhZHamIa+e6pjUdlgNO98VglN9rVabWP+8ZkT5yJzbpdu7t7mhdvWJDV++rwUQVLm6q8TZYKrFk5/4BYLmIx95jKdL71Mo+PUUM3c8lkYq82r2uqaXD3Os8Gwy+v1DhU/WI2f3OgGT4ORENnO63GDe1NLW0mQf971ej91njbbA7CeEaKYBqK3EUt5xrgX0ctgDLRVqoEKFmJc5ZOkxOmYe/VdtDBsfl5JTfYX4s95Rh4n2v3dS2aa9/4QahAeqDooAY4bAFWC7S6SRLQQ1LRwa3buo9tyhRa1Wr9Y1YPDhs25p3/dP1gOtAp+6pwbDM9TsAwFMUExVFW6gR6cXp0UBF0pGXjZsOLFKnr3mLI3R7ynfUnJnZRyIRHbV3P76pZ3HiOMByVnlALGcU+H0OW4BlomVtB0uRVtcNpSPCydkCp8kS8WsspVKZtHL3HkDp0sHhA/xDcTWGw75p4dmpOc+1q5KVzbrtA5734txTeodt2yQROXa2W/7hpMrdLVyIpllKpdq+tIVonuct8PcZAYgpCohvc9gCLBealjaWUqkuRmMKLxpJ3ZtlKbFELMRSKtWjiJ9VEBu127NPHlettw6HZ4QHnSt5vEzJ1JrNtuHAzNzd+HTQ49ijF48HJy7ZbaereL6nxttgBea1kJCb2wEanqKYqCjaQm1o0T7JUqT3GbvFUiVD93NRXPfYPx9maZXWeiokCZ+SJ5JVyUM602EP6eQmY1O+Uzaj5GqkmHvnoL96z6qp9TbYPM/ok51aEBpc5QER8wI3Cs2aVpZSqYLngyWcZdO27mYp8o3IVZYSpKdD/FSJmk3ahZqdLR2+OMaSde9qJMCfM9/j8h01tWpKuPe4GtTNWt122/Db0/H4mHiaCHd4SuGIh9TaVjNLqVQXI8KkJFnp90JellRpdNoSZ5WqHnEhD01fZ6lFELvQ37VhxYqWNsurkYpyGAAWnEYrzELoCVye/5DVbNjEdyqC4QJPloxFhFs+TK0LVvPdCI5dZsl6l4q+yz+CSDd0bsTWrm1enPi5StO62TxwhnSbp8XTREQuTlV2OVONtMHkaFp1/I8NTV9bxDh2L+IyZ+Z8XmfsD2BWCoCaptE+ylKkHvNfKqVSrJGqLxYcF8YJ61sqGhHC53HfyHP6xQqf6ua1OtNzw2PvxeMB8TQR7lBlfaNaaYPJEfeOw9OLeeUzup/Awyhww3jCMMDXiON2++i8dY5Gt9nIkqrI0Elvzhdi51xOllRp9hR68FeZJCd+Y29aTUf485NLTvxk8GD1Bw3Fs+g+JI3Ad4LDp+Z/OlwVaIzWZ/n9Trpblc24pdEZtrKk6srQUG6pi/m/LBQi6yerV4hKJS7kAfvrgepd+VzUFaflaWeAlJ9E1HvA2H9hkVYLAGXR6rfxFUXCecQ5/83+j+mtfLA4O+SalNaj98Lu1/hJ4YzGjwlxpUKSa15S4cFdFv7ukqUnfjJ4ZLra3Y90Srh0aHWz5Iku6eiXh4dZeiE1t1r3WFmauJ2qrGZfqjZYqVrb+TKeGDzhji7Ec94LSAUOGXtHM3M+x8adXbucmGEHoJbpNgu9z8ARu3f+MzdLVPWlZiXh85jF8mbthM+F7X6KZ9Fd3Sx5yFn6qnv4NZZeUJqtVus2libSFc73vERtsFKJe8fjdtf4YnUD0f0EEYwCNwy1ofsQf5ot4TV3dL7gFZ7HeS8Vu+J3H7I4v8teILTbbDa+jf+mxXLAG7nDfT6dioz2Wp7hByVNzv2Gyk4aii8qcbrOcs8lSyeCL3d1PFNDfViSJaLn0gbtR90R2u28n05cDbie7fcrvtdJSnxu8ITTNclNV38/FR0f7Hq8c5C/17Uqbvl79/a7zwcjN1Kix4+mU1e8ztf5U8Iqg64175mnimi79wmFyGu29I5G6CT86TsR7wGL8BTUHc7ezZUVorJICvmJrk07nYGriXS2N5tOJaKTAe+J3q4T1TwVERkfEv25hOvcIk0EDQDlEVcUqkl7h67beSmWfZ5Ipp4Ijjp7D/mFA1kScMP27d2Dl2K0YknfCA6aTYPZp9/ojtot61m6TKLAEXzNRW8oITV579YOYYaEGqDZsEnorp+wO8ZZ+yOdioVHB7srrWPFrYig66Q/ysW19K2w+4Ch7dnqNiQizqd7naOB8NVESvRQmcyefcXNFogtOuEOrLIsYhusHOrN3f38nUPjvYan+t2T/EFBtpG0KoP+s/2WZ0XHReUSQc9p0d+bHAqIWq0AUGskFUXCa9F39o9GEtl+B6n/I+fd/WbJ6ZxFrPpE1x1fdLkucHVLpnti7KihK5CIhe1+Sq6cfc3pv0pzOxE+22to7alu+Eyc7+055PZfisRI+OTPHWb2rNN9gS1Vofe5mG2wckh6x07jpu4TgSg/JnM/06oMj3ud+7ucVW3FofsJYrKjwF/gsAWoC7p9ruFtfD2fCL5i2dTStIJ6cPU6XXfPCa9QIxMPGftOmvkvhF+1bHqY+3zT6k1mV7Ye0Zi9Q5I5a255u7k/yayxCJewnrO0sFczxMOauq39fG3t39+2mrzd1NL5ea4/q9Hw27DUmju2CkMBiTd7Nj3M/ZIHmlpau3q/rHS6yDyajq4dLKlSBfr1qzPZ/cDqNqNdmCmiatLxs84eU+cm7erVD3K/IqNptc7i4veLpm9gV6W3yTRv63MKN8mGXeZN3K9a0fTwJsurQiHyHBdK2qJ6yGg/Jaw6dq6/q7Wl6QEuMzLZ0dKm77IccgUq3bUAsJyRiuKMTbhd8bq/37COVmW0nug097tuSKoJ3WedDqHrG7Ab1tGKpUnbaaddTaLd4XpeL+rEJrw7uT/JtAjnyVR+yxr2asYxUUteY7Dsy9ZhV5xdXJTla/Iaip8bDRZhuqHwoJG1P5pWr+sw2/0V17Gtm3v5UebI6e42Lq41reno4QNN1aTTF1z95q6O1pbV2UJASPasyjjybMUjs+W1wRaPzvaqQ7i44JKzR88fFGQbSauys3uv0ysaKAeAxqOznR428hVZIug0b2rJ9jtI/b/J1OMclV75WV7VNznI/cksvZ29ThzpYC9mdHuFMVONYQffrYs4n+bCJ989aZzu52OG3o0sqbri6m6lud3SsZfP7epJx90neroNm9aR8JntbUn3rEpzcKDykdmy2mCLp3mb3SX0jmP+Q11t/JjMA5lWZYfR0n8avU8oR4mjuOVcC9zNYQuwjKh1Nm/Q87yh9Kim3eUJ+UVd3xwaQ58/5Kl4oDBjo815tNB61ls94UA/H5yWWvM2h+dF2fyomMZ83FN4NFRjHHp3bIAtLAbN5r6x8JBBcl9tebTmMyHfc/KFaHOfL+wxL/achgLNDk/0nQEFR0XFxOc8yPptOxZz5VBligKijsMWYFnRbB0OXB4yld4zWaUfGJ8aEs685tLuGJ4aH9BXoY5tNh5yFgocGsNLodBrtdNa09neHCkc4KpB3d7nKtiKUKn0z/kmzizA41XlrDcNR3zWalxetHhtsLKonxgIRkeq8ktLJT7nQbT3G59gSViOFMVERdEWaod6o813ydO3WUHtv2hVn/RSFYFmsyMUdtVOaVvY7qda33daOKUn0W7zBUcWMXxqDM+PhY5X4+6WxWuDlUdjeiM6cXhRO4DoftaTygNiOaPAj3HYAiwvq1rNJyei1yY8x22mza3Zg1/TutlgPTjii8T7hEt1GO324VByZuxMn3UL/whsrX6Lte/MRDw2MbS9Wt0Ptf5wKB4ctm2jW0U2yerwhuLREfPalpYN3EdqQjPpV8fDnqF9JgN7pCnZVJPtuCd001OF63HWmj3Rad9Ra/aPZ7I6kw+xsb4nWrVbuNeq4hGzJzblOzNAYoChXdiJ2naDad+QJxyPBYeM1RqZXak1nQolr42NHLQK61qvN+zpGwlmVmRauiFgqnmLYyIWD3mHSG4IR8VjBgMp56c8Y5GZ2eq2vTb2ed7uM5Kes6bVfCowtK2y+55gSSkKiE9z2AIsN82f7PNF41P+kb49Bj0/8kWqsh22Ie/E9Gt5I5zNur6349J4kalYSB3riyRn3rLpqnXoZwMH26r1etO+4bFr0YkX9dqWFu6lmqBeb/VEpc0JGggC08kq1LHZVsQO9se5rB4eiyZDp0wdj1SrrULo+25PT3iHSTEwCO0oLmTQ3xL12TZW7eqixWqDlUn9qHUkmpwO5B0UW0y2wyO+8HTyjeqO/DcbjweGd2WyXbu1b2y0D2fVljVFMVFRtIWaon7UPBSMzgQl0ZC0gTPN7DO+qZt9+QFgsao+rfmN6LTfYWXdE61+h204MBMNDuhJ95N7qTYsbPdT3T4Qik8M7zOxapzsmkw+TCcvD5v01Xx2t2aHZybiGzlsM20RhYzMnqW/JTZx0li11S1aG6xMzYajE7GbofwxGdKWGPaOTcVmxQ9srwJ0P+tI5QFxxdzcHEsCAAAAAAAAAAAAQN2RvRb4Sxy2AAAAAAAAAAAAAAA1psRRXNlR4DSHLUid4rAFAACARqUoIAY5bAEAAKCBKYqJiqItAABAvSoSEIuM4oqVMy9wksMWAAAAGpWigHiZwxYAAAAamKKYqCjaAgAA1KvKA2I5o8AAAAAAAAAAAAAAsFxgFBgAAAAAAAAAAACgnmEUGAAAAAAAAAAAAKCeyY4C/zaHLQAAAAAAAAAAAABAjSlxFHfF3NwcS5aMTkW8evVquggAANCYFAVE+hicJ598ki4CAAA0LEUxEd1PAAAAovKAWM4oMAAAAAAAAAAAAAAsF5gXGAAAAAAAAAAAAKCeyY4Cn+KwBakfctgCAABAo0JABAAAWGiItgAAAESRgFhkFFdMdhQ4yWELUkMctgAAANCoFAVEP4ctAAAANDBFMVFRtAUAAKhXRQJikVFcMcwIAQAAsBje47AFAACABoaYCAAAsPgwCgwAAAAAAAAAAABQzzAKDAAAAAAAAAAAAFDPMAoMiyp8bEVhx8LsE1AvUtcD7kM9nfp1dA+v03f3nvCGE+xdAABQ4Ja3m1amubq9t9hHoE7cT4RHnb07O9tauD3c0ta5t989HkuxtwEAoHThQa4qzTc4yT4BdQPdT4BSyI4CH+CwBakPc9gCAECuVPBIZ+uGrp4T7uBkjL4Um/S7Dlk6WgYx3g/1BAERAKoofdXd09rSYe53nQtGacc1EQ2edfYY11lH0ZGFxoVoCwBFofsJjaJIQCwyiismOwq8msMWpPo5bAHqzJ1o4Gx/z1PZK1CIDR2dO3udo+FEmn0EmEm5U8sFNVD4ibxi7DwWRG8VGoGigGjisAWoN+nEpNe5v5u/AiVzDaehs+eQO3AVF3HmSHh3sjwqRXfjjH7eCfRv7nFfZ0sA9U1RTFQUbWF5Sd8Ke0/0dj/VIYTPxzu5eyCiqfvsM8Cg+ykD3U9oHEUCYpFRXDHMCAFZ9xPBE93rHm7r2ut0X8xegUJcDwfPufrNHS3PeCuvWPWH5wRhB3sV6skdv+sFvsmhMb40EZ/ldvdscibiG9qlVrO3ABrO4xy2AHUk9b6336Bt0Vv6T/v5K1Ay13BeCrpP9HS1rq7CbaePmH1cVcqJe3awl6GeRM7aXXxLq93miSTp/p6NT0+c6dMifEJ9QUwEVSrqfaFTu6bDcsjlvxgWwuf7Qe4eiLbVL1c+jKkfoDUpJ3SUvQp1Bd1PACUwCgycexGnSdd5yM9HX4DypMJBN0uqNAc9vhcNGhp41c3ajaY+b5+OWwIAqA+xC73Gxy3OS7gABSoUCXojLKkyjrw5bN7YTBfUmlbDnqGh7Rq6CABQD24Eere2WV7B9ZtQKXQ/ARSRHQV+ncMWpP6CwxagHiT8+439F/gQrDUdH5u+Tc+gZc6hJWNTvjN95macRZNqF59azhCfXnaE2YtZA3r2Tp1LJqIspVJ1P7kJhQbqGwJig0t/d9DytEu4/mRz30h4ZvYDVu/PJuPTQc/QPiNqQimN+S2WRcxNj3Bb+A5PnL3K+HY1xuhnOhm/wpKqjYZN61kSAAhE23qTjgzu6nLxN8poDH1nQjN3WbU/dzcZj054jtsQPnOh+1kIup/QUIoExCKjuGKyo8BxDluQ+iMOW4DlL31xyPYmPwSsd4SnfQeNrQ9l6091c/NanWnPkOd1U6F+WCo27u7f29mxgc0/tE7PTYN4vVrTIIqe67ozd0qKxKjwyHT+ftvwMfpC5rHp6avefmNmjqmWx7v6L3AXOt8JO83cpMcbOnpOhEVbKUxTyKYgTARd+7vZ72pp69rvqt6patGUiMe40YP7ieCrvd1sNsl1HXsH/e8XyMD0jYj/rJPktmjeSZrhLu9koY0Tpo6i00KloucHewxszud1+p7B89Ei+ylxxS9+ympmw57KPGg1WHSOy0QiyFIqVcvD7DqmeSS46cCyG5Z5HjqdirrgXGDCj2JPxk+97x/cy2ayJju699Wg6It8VmdyIPVNp+XxzOfIb3ddyUx0LSkho9HCc1+nYgGyd/jN29DRvd9ZOMOF4scXV3Get7Q93evCBYP1RVFAPMNhC1APYp4jdmEIeLcnFByytmvVK9kr6mZN62Zz32tjfe3sFTE2jzBXKWUqiMc7i9QtZSgYIrMKxdZs7ZoJgvei3kNdXOXY1nUowIXPVPiEhavLSJByhu9wX6EqizWKlFnHplPRS17Xkd5u8WMPuFhjPxuIFdg4IX9o1okDTab9cMxf7CelYsFRZ+9OYV1sjsvzkWKPWLiTEO7H2tDSwlJFkfCUeZYDP5kmCdM9/YV/EaGw7VF2eRCQJqJLlA/rOooE91tevryy4ipuhs2b57AMKYqJiqIt1L7Ym3a7MARs9oQnhvbotavYC6pVzZpHDeaDw2MHCw1jKqv6yqC0+1l2d6OyWKNIeXXs/XSM9AdPZB4axHf2WYa/6g0XirdC/tCskwSaebtCqdglSdMoE6kzO9cfuVUkfJbT/VTUBlPY9qhG95P0jo8J3XCyefIDLGUOLMDyVSQgFhnFlWAnivL8DoctSBV5C5ahmZGtrDAQxtdn2MuliPn6NstdoaMxPD82z98Szwt8NMRezBUSPpR3fVDcK1w8xJ/8zJ4R1Ti8HrNk64wjkZBD2hU3nuG3UZhm0eSdnj5j1bIlEY1h6N3sJdKFFD0ZKyaa0nG3L3435CiQjRqzV5p/4kulCtHu8eVmuJDDjtDtiYFCO4t8KydXM5JTQ9vk9ixh8txkH8xXciZQyalTpgJZTa03DUfyMlz4URrHu3MzbxXaU+2OELuagM9q88hbDklDUjMwEc0pIZqBYO7qZvw2ubPo2h3DU/w1C1nCz9c4puZmfHsKbJ3+aKhYMYJlRVFAVPRhqH2zwQGhCtH0TeRVCLKSU8M7ZGu+gnWLlHheYNkKuWCIzCoUW7O1q+aox7NbUjkaz0yFjkrrwq0jQsQpO9bwil4LLFZeHSsOTAVoDI5w7jeEn3Q5OXHYUOAnrbf6CuV8kaiRIdvaUZAJ1Iy/r9BmcTSGvrfzm2AK2x5llwcq5rMVOvmRUTC4i36++a34bNhR4NdpzJ4Y+zjUAUUxUdGHodbNTkjC5zsK2sXKqz6J0vopSrufZXc3yo81vFJ7XmXVseIfW4jW+lZuhgtf2eGZjo5YC9zXojEcnyqwy4tEjQyHfPgsORMo5W0whW2PCrufyamTRpkyXnCARWFwh+WvSEAs8pYY5gVueNeD3nGWVKms1u2ydWKuG16Lvlt+JsRE8JWujmf8RU72LbCE3WyRnr0N2LeahNPOnMApPz8JHy92ymrY6y4wRXIi2P+cK//zFbk35d5vshfIxoTXPOgvfHFNYbGz3ZZX5LZurG9b52ChnRU7a3NcyDk9GHFu3SSaIWThpMPHjJsOyM9Gfd3fqzMMflfu3G8iPj5o2VloT03abadzssLbs1O4ZC8j4bZszikhicFzQXFexEYtHSbhXu8csXO9xv1e2Y1PxAPHLN1nC7wfPmJz8bf9AsBylQ6ND/JViO4Fi4G/iKm4e+HBrZt6z8lWHqRu2bRlMFLsqpeFlThisQh3CGUEPm80HZHWheMuf4F6TFGsqUwV69hE0L69PyATcMcOyjx2/Lrb9lIg5yelLvQWiRpVxIUnp+wtSomg8+kOyzm5t5W1PcopD1wTUbjXOwcJ7lut3htsKV/6Pbd1u73Ar0t4LUf9uKIJYLlLXw4I4XNjv2VLqXfwV1r1LbhyuhuUolhToerVsTH3TotTLuBeG7Zu7nFfZ0siieChvDB9J9BbJGpUUYVtMGVtjzLKA9c7fiEgU4gzAyyWY2HZFmL1BhagvmEUuNGlohHhDortBt1DLDmfiHOXUIXpn/NM3eZOK3yQnPYP8KcWE2/ahi4uXUeW4K4JnXndSJcSiQQ9yTl7eYBNEn9lKpZXT0YmuRtc1ltH6OO5k6IriCc9QUV9y3mdH7STzpXG6AhyzzJNTg0Jl2a7fZJKXN2yp2/EH5qOJ5P8KcPZ5JRXuOwofDwgMwwcDmfCqsZweIw+MnXmDf40ZMI1HhLH+/RFT382Bmt2j0yxZ6wSs8nkzFTA49jTIm2siW6bWrGi4wh7lbDr2Yu8zI2lWakL/UI3ks8BspqboWHhFGvYfsQjF6hd3L3YfPGLv2XjT31GThbICs1uz8zc7MRB+qlMcWAl5I3sWevzEWFdt7z95mwZ557VzjYvNuHIXiideNMyeF6useSyZ36d3uadSn5ADo24b5+wdUPjVT6bAACLLhq5yFKEUV/io0dSgUPC+UjNNsfETVa1xMPDwiUzk3b7m7JdlEWhz1wee21EEj69M3N3QwMb6UuRqWviCEUpiDUVU1DHqlcZbcc9Y5EZUfiUVOZk6zyFBq+J8GQmTmk2D4zRPRUTruJJnA5If1LM96or+1f0A4EZYW2ZOS5DvlM24ypJ/BTfWbxijcXPXlapzlmyd8Py6GwbnCtOCx+e+Bwgbk/7hEvJEt4DQ8F7bCGXgrYHpag8JLyH+CYit3k0H+7OTLyUvb6p6FiD/5idfJ0/OpLvDrH1Emd9sgNAALBMRN8Xep+qbR2lPrmr8qpvUSjrbmQpiTWVUlDHqlusB0d8YWnv8/aU5zmh9ynbqbkS5nrUWusZuqeSoptIwp5Lkm/FzjuF8PnimBA+P8g8YSHkH7ZtU1ej+1l5G0xx/05ReUhPOm3Z3nFm82Js85IRD3+hdLFT3YqDOzQqrmAVUORa4iJvwbIzdVIUeQ8XudNCQnwbLFe1Scy+08e/W+yuRuEe0oWYEYLQDUW4l0QrMnm5vyHcFMPfSyu+wZb8KskdMcm3bex17iYa9mqeku9Gka6Ldq6ySO6xl4kit44KSFxhH8+9NVicw2Q1kptkp4ZYz438WMcUezFDnKumN0q5eUS0j+bD8j9DPBWJJAc44nc1ksyU/ihp8ZsZ2cJez2aFOKutPjpYLPxAHVuv8Df5u41mJ17kS7Ep9xbU2z4reyu3WIrLQKYYie++uTZiYK8XPS5gWVEUEBV9GGpdckwIDPm1hBx+HI0Q5q7JEr+rKXLzo7hmq/6MEITuOA0L+R8TVi3U52XFGokyZoTIqLiOFX9FEnClcU26p0QNp8zERCLibw2UEr/F+2g+BcNT/j2efFczQxRzCYVtj3LLg7iJmNeKSPr2sLdyi664DBCSPBe3c0q42xeWCUUxUdGHobYlx/axw5kora9BlF31SZTWWSsUIrMKxdayuxvlxRqJUruf1a1jZyeEmCHNImlck+4pccNpt2SeKPGvGLjMXixKmnVFCeWh3DaYwrZH2eVB1P/dOJC7eZEhoUBIWiwKgzssf0UCYpG3xGSvBX6WwxakPsJhC7DMpe+Jzlk9wP4/H/FtsJre/eacWSTUW7p7+UB8LjRV1bOXCuyxW/nOJ9Nn2y40EeTpHefd5rVsgWh+WHhMS7rgU03KRwKJZ6BdOLupfnA1S5VK3fQgSxWh2e3xHNaLTqK2tGxgKVVCJblge6XwKf+h3sHxaKrKP5kjnopkR69VlAMcbdduPlgmfGGZk8ztDv9r4uLX1CT/PADdcZsp51L3LTZz7np5kfDZbBnfZzWKCkPGQx0GPtzKl3D9Ub97l2jr1EW2DpYrBMTGdS8lefhC9olwxcUueQMsqTIdsOpzJpFY32Xh65aEL1TdW08UsNr35F6b1ffZgk+IzaUg1lSsCnVsKV/RmD2jA+I91aLhV5pQSeKjWiXk0WDfAXe46NNsypQOBV4WmmC94hzIUBtMQhPMf1kuQClqeygrD5FvurPbZ7Nuy9m85o4tfHD3hyKyWyfN85LaOVDfEG3rSDolvjN9ZRNLFFedqm/BKexuiCiINRWrvI5VN5XSX80N082rRT1qSXxUrxSFz4O97sliz1ItW1XaYIraHsrKw61wINs71j3Tnbt5Gzu6WUqVIPGTJXNUPrAAy0CRgFhkFFdMdhS4hcMWpH6DwxagEYlvg7Xoc0daiVbddpZSqUJR+anfFpa2Ja9SXt1UQhRWqboMpUTr6ujoelLajmkfYOdoiMOSCeUz0onwqKt/b6fw7FTpXTByOrYacppLcjT6LuEcbiJgN7atXtPWtd/pHZd7uLletMXznJH27WIxXjwViWGLPr8zqWkVbhCLXIsXbswZDdIAqTG/xVZEVmV+hL1KaR/Jq9Cam2QbntenhfuhTnetZjnNa7GcY2+qVPGUzH1nXVvEQyEq1SNmH9u2ubm3pA8GgGVLUUDcy2EL0IgkNZ/xY4VqPmFmiUjs5lJ1Y7UtedNDrX6wpLBYeqypnNI6NnU14D7S2y08Vl46CYOcJ7sMOScCZbUa9gujpeFXezrWNK3TZx5uHr5aeFdqdgmbPN8F0QOsQXBVNJHX7kITkTyq4zuKqnBU5qZWRW0PReUhNn2ZD5+uAuHTLGR5/J5MNz8vz/WH2aYRA8UeHwTLiaKYqCjaQh2qTtW34JR1N8QUxJqKKaxj07fC3lf7e57qbBP1Pu3szWJyw7S81s29wj6ddPXoW5o2dPQccvsn5a5GKqP7WZ02mKK2h6LykI5O8QEy8sImltMCUZ5fkQ6iCxQOLMDyVCQgFhnFFcO8wFCGdFo4OVZwXFV8pi8iV0uBQqnIq93rmlo6zL3Os8Hg+3wvq9oeMbvfyfY2qUQ0cLrfYtzU0tTSZh70v1+FgYn0PeEquuZVhaLhQy1CELuTWuxCdCchtDUBqmQNhy1AIxJfANVc8Kyk6Nof+TEyUOqGv9/Qsrq1q+eYy38xvGCjA2r9856R3ZI+YGzS7dzb3dG6esWGrt5Xg4nKr+dKp4UrgB6crwkm21FcOImEaL5sgCIQE0GBWq/66lQq4tq5rmlNh+WA030xGF2w3qe6vc/zunQI9XrYfaKnW9+2+oF1Xftd1ZgRvtbbYKk7S3XyAhoORoEbnWatcOmJKhJD3VOr0pGXjZsO+BdnBzVvcYRuT/mOmltzz5ImoqP27sc39cg/WRUAoCE8om1lKcI/vVR3vcC8bvkt+m7n4jwUZVWr9Y1YPDhs2yq9GIe4HnAd6NQ9NRhe0scWAQAsNY32UZYi/Et22S7MJx0Z3Lqpd5E6ferWz3piNyeG9xnzwmcscLq3U9c5OImxfYDqkB0FfpPDFqSCHLYAy5xW3I09H4oqvsozOVugQk7Er7GUSmXS5t9vAUpd99g/zz8eXGs9FRI9ejxnxvoqeUhnOuyZjs8mY1OZx5o/Jt6LMffOQb94Sq/KpO7NspRYIhZiKZXq0SUtRPNMpZ879QQ0FATEBqZpE+Y+UgXelZmiTVaqUPRUxW8INV/rGoTPyqWDr9n4h8prNg/4ovFZ+lh5IudpOdWh1my2DQdm5u7Gp4Mexx69uEObuGS3nVZaVOTdLdwEm2YplWr70jbBijzhMIOfJApgXoi29USzQVT1XpCb4VRerVd9dSI2ardPsrRqvXU4LOl9lv5wttKpHzHYXhub+WA2Hp3wHLXq17PXMxJB+z5X9cJnrbfBijzhkCO9bRcaTJGAWGQUV0x2FHiGwxakLnPYAix3j3WInnLuco+XcrGMtnU3S5FCGLnKUoL0dIgve5pN2oWa5CgWHBdq6vqWikb4yex1x30jz+m1zYXuY6k+dfNanem54bH34vGAON64Q5FCwbNkmg2b+NAaDBd4QEwsItz6Y2rNOyu80Na2mllKpQpP4yoFkKMoIDo5bAGWPW3rk8IsdpE3AuH5K0VNq05U871XoOabEi5ZNbUt2BSB6cngGEvWvWhEaNhYXW85TI9q1KU9yq9SqzStm80DZ0IzyWnxNBGRi1MVxRRxeLoYibKUIP1eyMuSKo1Ou+jhU9xEDE1fZymAfIpioqJoCzVO+7jw8A/VFU+glGs8a6Xqa5zuZyr6rtD7HDo3YmtfrN7nSrXmUYP58EjoWnJaPE3ElcBURTG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- "text/plain": [ - "" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('images/pipedoc.png')" - ] - }, - { - "cell_type": "markdown", - "id": "d15d7aa9", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Part 2 : Custom operators, 3 API, and custom code\n", - "\n", - "* Guidelines to add a new converter\n", - "* Initial API: verbose\n", - " * 2 years old\n", - " * really verbose with estimators using estimators\n", - " * options, opset\n", - "* Second API: much more readable\n", - " * operators as functions\n", - " * easier but still too complex\n", - "* Numpy API: python users\n", - " * Users don't want to write converters: too complex.\n", - " * They only know [numpy](https://numpy.org/) and [pandas](https://pandas.pydata.org/)." - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "524d1013", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "add_notebook_menu(first_level=2, keep_item=1)" - ] - }, - { - "cell_type": "markdown", - "id": "b0155ff5", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Guidelines to add a new converter\n", - "\n", - "The best is probably to follow one past PR: [Add converter for KernelPCA](https://github.com/onnx/sklearn-onnx/pull/737)." - ] - }, - { - "cell_type": "markdown", - "id": "dd33f527", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Why scikit-learn is more complex than tensorflow?\n", - "\n", - "* [GaussianMixture](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html)\n", - "* [Mixture Models](https://en.wikipedia.org/wiki/Mixture_model)\n", - "\n", - "Formulas... do not always help." - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "decbff29", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 31, - "metadata": { - "image/png": { - "width": 500 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"images/gm.png\", width=500)" - ] - }, - { - "cell_type": "markdown", - "id": "a3d52aa0", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "The code is needed and it basically means converting numpy expressions into ONNX." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "f1ac2d26", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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qqI7mdzSfIn2TM1aTvg1rUz6ubNMEeqMdto/TV5KMKXn1ptyChiHDiZPjraQzBl9ta15Vq3IrLKIKVNBQTLUR8lquST9/ijDZdMXnaZaOOi5pg2XvyeYR5S5LITUbHNotemVqrmZwiClXfVFuGtvOmvSMvSfb5prlhRgcOUUNjLO4zwccA9o2EYK76WTO1g0JgY00eYLMYF2oFH5UbredsD4j53jHuPLNEg/thqOAnzlkhAsc3fIKU5dzyJFa27TH4KndIq6fW/+UP559OtT8lTVrk3TgBmcgPd6ibnGIBhqKs1LXsv3JdyhWM3fmTA621JK+IvzMSfu4uF51+0rTZ44o3OCY/FgFw06bYFXQRdZh08AtX2k7fwutVnEw15Wz5JX2yk8qcypGL26NevQY8ajLc6Uj2pxV1Kg7RaOBvlPk40C/KdHxMfqmZBa7E0DwFN4/1euw8u8V7DOue6GQZ4XmO5u65NOMrabO6Ecdmap16K7Sh+PXpE/6mvSs8rmcOWG+kSqNnLKIq33SOMkfA8BigRQehUKhlmsBAADAUkJDn23FBVk0OZIrvaBLTmxYkLevvNKi6ZucWtkrRTZCV3GK8ilaKUXdqtzH77n0abpmHXXypcmqjEOx3uObadKqKpYncmayVM/lfamKdXgpD5zujMWarxsc1YTQp5f30cBRVYavGFq4wWHvQtbhYs27kLDnspyLGW9E90Zw9ADnsKuRcF/M158YihnZQu+xDP27mZDlcPNTS2g7JI5/cn6r+gi0mSBLUbWVckz5VvGz3aYdouSci1N8FTNnDktFtaU8K8ydOeEHx9zHKiQzKTwfzC2n3PSRRGg+IK6TUTvCHrIRzi8o0o5epkMR37ObeapWiE1LcfpOpR2wapu5K74phad5uzVFLthQfo9d+1Ymam9YGuas0HxnU5d8mrHVTKTw8fk23QdHebabOXPCfyNVYa+bd0n6rADAIoEUHoVCoZZrAQAAwFISyIbWZpR3kIbIfmHAnk2iGUX+GAjyEraWNQ/SDGbKVUUSmbd5xiR05JPb9+Xbe3hCI3i6bSS3etvWwxYQfDup+bVdHoEmZYJ3qLm82PAWrM4icftGKby/u0B8rY3FzaM8UCIbOZ5XdYM90qAHaJClTl3aQ2I10gaa5YOCp62UplobAyleYHCSM7+kU0T9U200glynSLvYQWV+NTTJdscvuLscBUUR39Uz7ODIcao4zi4yzsIgvWuiIhebvJhHDiHVyif1izvTWkZTe9UbIQk2ONHDo8n0fV9183bbgnegqbKgQQqaXZUkK0zKru3xsqOe7HGwqyNZDdJR9VSSntSHOvj7Tbm/ylBH8976A3lVTUPjUto72XOKDE5SsZO/m6LAGyrvz/hfreLG45RXMrjggzPamL/3ZPMg32EyS/pL8pFJCIS25s6c8INj7mNlEj9RjTvSTNV/Iu7J5tpB/pgQWvLF3TO4zpGc9rmjl/4JwmQrjXrlQWaXTNL3OaQp1YLHeYwMjjShPnoCATo5eej4PO2gFwCkb0pmmEvh2clmKaXfIUX+qYEGKzlw5cTw8GcFw86NYB1pjJ41TuHFEnepZYC9Ea6TZB3xexddw9SZE9k3UsJt3yG+bv7VwIcOABYFpPAoFAq1XAsAAACWEhb6pOdfVWZzQ7UWcaGc7bL8LqWoQ9msgC1kEycnL+bFxW+u6qNPBHgcGcrY0e+ybRQ3q55AGlzQFJ6FgxuL20zNrg2SpbLJv9rlrPdxsjQblA2OeqovPShlaDvw5WZxHDJNHlUwJgZHiumL26T3RdR2WPyqQOzImrRoB024SvqK6NJAUZDBiZ7ectLqJPjgsND2beUfXhCDmneHDo6qKQ09UlXCboAOjnI06PEmZdv55G4m2CBEMjj+DvXKps6ccINj7mNlWsgUXvwpnjafUWyWvrrqOgcNf5Mzv1Q2NmHz5aUzkLYPSjsxRB8EeOzbFAFxtLAAXT0hncXE8l82RIAdndE3HHb6faq9MSk9u0JODNeeFQw7N+afwqcXXFO+NlvIv9DUmRPZN1KKjnnGPL/XAUC0IYVfwBp2lWfcO5gx2OTWPYVCLary1dfnZNbeuqJdvmxr6EhOjnXgqW75Ui8AAABYSowjIZqAyxNjlYG7Ibp+kAqEv4On0sSHB7RhVjBBU/hZwVlKe2Ksycg53tir6uSuZ5yl0pQqMfOcNpEUrlrJcp5UmknEyBxYOiE9eXVWsb11aNzk4WmYGBzDFJWNPH9rWAv1LN00fBa36QYh2OBED7ucY20OelQsXi/TJZLsC+WR1858pxdRtNOraZf/DNavXC5dCm/2eEOtLIy0BJq5yyWvbObMCTs45j5WpoVO4aWZ0YE/BJm6tEf5UGScU7PN8vnRoaaWa7/JzBd7LfU4hDnGUIKn8PxPFgxLtX64s4IxOjdkZs4cVeBuyNyZE9E3Uop9m4r83AOAmIphCv/0q3evv/bGnTbtcpP17NsDvf/8S3ELvN4vfKpbZ2nV6NnBg+n3xCo969Q8tQTL2fgbciyBqunUrPCqVk8NOd4+p3b5K1S++kt5KXHxKQUtl7VPRaEu2DNVP1sUtGtWUNfTM/WH309b96bZ9WNTQvvulPjEhA+L3D7tU0u7AAAAYCkxjoRU2W60UvgI51HSbRqm8CJyf8hMdhdEscgtIoPdVNA4Sw16RHQnI0vhRfL9IcVKXp1VGfHdWU0MjmHCqHqn2L4ZZGTGgxB8ebTQ7W9zqLuNKwXbAV3O+LQxS9GUhobygX7llGE7clLRT+ENO26TklemXxvmzAk7OCazVLPCJtR0br7UsYQOuPp+raFSeL7ZZZzCmzgrGKNzQ2bmzIlWCi8y/42UYn/PEfm5BwAxtUhT+Kd28rWqWswpfP/t6s+GrEd7tMs1tUBz4b931g2UbBtsjO2rIIXXLH91qrc8JS7+zczT9ZrlUapIUvip2pK3VCuHWT/CGjz+um3Viqojx3zq5aN/+ntx+dFi1XL36a0J8Ykph2sFxcIlXwAAALCUGEdCmlYeQTNrScjEXOIi3b3NJzhmtimMuOqP51no5NOU0ghuQDp+jqZ1UZkLHyB4B1odBVkbxB/w4pKyDZvdB2VicMKn8N5Gsm+LaC48a7MTIi7sLkkyNReeT83mM8dpOxr1rURpLp+YkHWq1yufBbpTKKLjDbYya/tDGr4r/u5Bs7KZMyfs4Jj7WJkWPqGmf2HAOhdNNuTGxefW057jEuOcmjWB4Weg6iJWjC1wCh/mG5eZs4IxOjdkZs4ckyl8BGeOiW+klImLhQCw8BZnCn+q7+dvXH/tl64L52aWxMzTzj6SRNvCpfALVCwfj3UKLxeb448U/tUooXNvSnziui+qFyBrHj+9VfzJIESqPnk+U1wh4UNr75j2qejU1L82vLPCtur1BmezvHD4d7WrVtjS3++ZUKxJ6krbF7+Ii38z/5tXqFEPAAAALCVGoY+vhdzZT9F0O2wKz5I4+Xaahli/FNNtiCMIknwdBUEDTcMgTEq9lTdXJNgtWwNt1s0kYgbc9sijQBODEz6F58GuJjwl7Xc0Lb8lQQYnalh0/nbBtWCxHl9Be69RdstW9R1TWeNy0vmEhsWak42PwzX+kPBPXfpUXKg8hSI63mAr87OiTTlleOryPtXKZs6csINj7mNlGjt/Qkao9B6t5INP/qG5Ha5xTu0fUt2KgM2V1n6sYmPBUnjW3l3VnEfPzFnBGK0pY7uh7FMkOOl9fRXbCZ/Cz/HMCfWNlOBXXJSfMgBYBBZlCu85cmuRT37XFFJ4pPCvSNXXviP+MJd5KUapt7rCpvDOEtKIJqYtaHzt7x9dtcL2zs6+KbrE96dvc39mW/WzM98qcvlAPT1Gfst9a3/vK9OXBgAAAJYSFgnl1o9MCTQVEjwdJVtJgwLLl/ItFsOm8Pxmp0mb9zmUU0E12K04ExO2ljUPevnLeYeay4sNp40HTeFHGwuKHG3SFkTCyOX8jTQm4wuU6AHqs1S/y0YamCRbSjvcLPgWPG2lGWQae2RZ6qzzRF5Vg8s9JR224O09QYYrwltEhh8cEyn8bO8xEtiJG2ljXZ79gru1zELa1huNZLDBkXnr92xIiF9rKQ8+PTYkFp3HJWWUNEkd8wXvQFNlQQM/CnaFIC41397Dj3qyx7GPvDW6W7bSG9gmHOoYIOOgvSums5SM3rrDLaSfhl8YH2yxbWcN4pUHHvZ4lYKtTOfvi7t3le4BPZwM1go8wjMn7OCY+1iZdqOMDrW1OXgHcHa/hKzyykz9pREe/m4uuSZ99J667J/S802+nwGfEp72uUP5RwnGhG7blrVxSRtyzs1pbvWCpfDSZ3Pl9kr+sTJg5qxgvPVZZIjUs+YD2LWZ5Ez7EBlkwdN8eDN511TbCZ/CmzpzIvtGKqIXaULfkBYAXoZopvDPR6r7PrF0J9EGMkm/7F6TpE/hp28Uuv7bWrLCa/9P9z9v7/v2gl/xLK+RwpsLk8L7nJ0Vg9Yt90iGnjFUXubqf6JZp33itqvmsyGrha6zbbCi5tZoIIBj4XuQUjR/Z7ltoHT5uNtVSlLsdvdfBwrFF9ox0OIWB+puaca9g5ahus5Oac32iTu954sGC8Xl4nYs9wo/62+6HXhW2yJGXerUOPyBR1AhU/h2bxcZQL4b2wZrzt70ahJMX1fPH/tLtgV2NVDmLyTQY7f09Qid/Rf7y9mmtgxWn+2eVq5G3y+yn0+6m8r44Vs/7m+Ux5DWw5uN0rMHt5DB6XnYrliBvpvia/m+d53tL2HvRcZg3bUuxTpmiu7zb1yj4hBdu1vxsfRyFbfU40POwGr2rLi3uQMOgxdqH229W5HLB9m6Y7Dm4s0JeSPmBoeWcGWX+HOD5cQZzXK5vKfkLu2rUzJ+c6BtRLeO70Jb2fspq8nPHwnrfpV1+Eiwm5qGT+HJn1fHNoWfFZqvF/yM9KU5/q/iQ2/9L8V/2z77Q7BvPu7aTeIuZZ0N0jGf3NX25wlJvyr4JhY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- "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"images/codegm.png\")" - ] - }, - { - "cell_type": "markdown", - "id": "92bdca6f", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### First API: very close to ONNX API\n", - "\n", - "Class [Container](https://github.com/onnx/sklearn-onnx/blob/master/skl2onnx/common/_container.py#L203). Example: [linear_regressor.py](https://github.com/onnx/sklearn-onnx/blob/master/skl2onnx/operator_converters/linear_regressor.py)\n", - "\n", - "``def add_initializer(self, name, onnx_type, shape, content):``\n", - "\n", - "``def add_node(self, op_type, inputs, outputs, op_domain='', op_version=None, name=None, **attrs):``\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "044407a8", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 33, - "metadata": { - "image/png": { - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"images/linreg.png\", width=800)" - ] - }, - { - "cell_type": "markdown", - "id": "7a64e88a", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**First API: major difficulties**\n", - "\n", - "* Developers need to implement python/numpy code with ONNX operators.\n", - "* The API is very verbose and hard to read.\n", - "* Developers need to write code to convert model in several opsets.\n", - "* ONNX is strongly typed, python is not." - ] - }, - { - "cell_type": "markdown", - "id": "9489c8e5", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Second API\n", - "\n", - "Simplifies:\n", - "\n", - "* names\n", - "* initializers\n", - "* adding nodes\n", - "\n", - "Example [k_means.py](https://github.com/onnx/sklearn-onnx/blob/master/skl2onnx/operator_converters/k_means.py)" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "1d1fbd2b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 34, - "metadata": { - "image/png": { - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"images/kmeans.png\", width=800)" - ] - }, - { - "cell_type": "markdown", - "id": "1eca9f56", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "One class per operator/opset is dynamically generated based on the installed onnx package. See [Available Onnx Operators](https://onnx.ai/sklearn-onnx/supported.html#available-onnx-operators)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "f88e5653", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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pvHrzSKnZKq/4/bGdcDid8nTlxtRWG2tLopP52Kdb8IgMwNW7q9FB64acbQ0ZNy3lFLCkz5K0Pr0SnRPnctnq4loszvxFWPR+R4DLcH0VkvN65dXD9LuoWau4C/UGHTndhXqHS6/zlaNenQkId5yc/jjzj0iLkulP3HtdaupBvcF9En/16ULSZ1dIrbhqG7zp8prGtzB9dutkDMqe03/y7sDzG/ukMIurTp9p7YRHWVs7WvddNfpd1tvElpvyf40X9fLW+AuxGu69hwsv3x7EWhixVZOf7/phq3m4Nz26ECwQWRiZ2qtGHlnLxoC+ybsn/qq9CHBT7LmVTfffP5oOnj4llSmuSG319J/SxssnYjm2B0NLk6WavTl1erRW1tcJfja6UdypvPn5Ctpg6nnawnzXo2AA3Ak9ER0631ervGpbEALirwYFr9cNWfwxSycpX3UyFym6bUfe2HxbeuotazU4Fp9z2o8LBbGyWy4/qNaPNzrdd4KOjCN9cwNO3FmsRtt9xkCpbfGgc16vFqdU4sO9wjN7+83QHHCxa1h8732wE7gn/a+mvzhbEZ2dm2veQ2xvy9ru8ivu/ja6J1IusXPxxxCd1w9m/dpCrvC/tejse8ktmaN8IVOls3Wyu7g6MiRCvFsnGdtar/kzLrvjUmV3jPjdpQ10it4Y9ob6RaPiVoEGglVB7z10akHbu9b1bSMVkqtwVj88KE4Vxf2WcNFax/N+R/7+4vjb7fWyWLJQ3qgj+WOe5KHH1H4PKv99Q1lzYW7pIe7q/O/iZ95Fo6arfeNli7PJ0iD+arQwTEs9qP5K2skaNfyiUvzevQZO8kplfqp90F/w0Qtk4/lKtbJlSp/FTiS4RA5D3Le3+86bteWZ0jMV5pIL2PZOKjnopGt+Xc+vqI/znj3Mrh3H6kg6v14U1B/Mm4oOWjK0OG+d4NX70u0Vp+yhLbavqDA43/3yVPDV5/JPI4tue9+RX2VyXx0t/5349CXxHg6diyb2o/O2ILsUf9U9r/il8N/v3MmiKiKqplf9uChzUHbCctNdFlauT0r6rHP7M3n/IrZ1HbXuuwVtRM/3RmVLzLXsbO4NKieqD22557uxb/fiW83Lm/rvcb/+07+nCvLJs9oebnyMBIzWyfqEjF6F2UPxZ1+rWfvDu+9zQ6X5nYRGiPsEST4SV5dFJNRsVG31z8qn184d2Z9/NFR4FFSv/c/9y5CxOnnvXZmnU3v7tdNms1Etb08Gv89LT5+JzrfOJlsgAJCoZ6JD5/s6leN3wWRbfX2l8EQ2btAZ9xpL94Y31g+DSrCestEq5R2JHbm/5FN/XiZEQn30ml4QcdNqW5uHDTcurG34K8rnnnzYj0Wib4dbI86r/cXJxern5mmzfrw+I6pQuaGN8ODZs6bzBmdbW/E/t2946x//L/4WfnPVX3Krvzhdqrn/cq06P+aH48Jv4czUeSj+OnXKs/0Zd0G6Bw+DIChi6KkaiRmuUifSVoLTU5mt5r737NR/ybk5P6+5y+TdeyjTiNEHRYktGW+JtMRKp6NRXvVXrHa+nd1w1Hb+Mu9dBH00k3Nl5Np8zhb9BYVOwXRBgvTTM6c5539ccDPYZy9VFZLMVzgrvdeJ2mwXLZg6cGDiU6Q2cy5X5Li1K1DhxtKnPw6aA+m0hL72nKmLRk1X+37/srYSjCed+79vw+WgU+y89UrCXOF/+eqX6L6p6TP3KZF4g33pABGYnLJrxm/gnOzmxXL/llVKvy778cU9qoNq3dtlcVUUd/4WPR4ntgYLNBteDV9A81cZhDn5ofXjzby/3GrhVTm64nP7J9VN+sxd+NX/x59ObHuVgdPPlb1pPxr2L80fmsp/+cTiodva9UPG0+D7evq2FoT7M/m168+0vOXFE9rISjehzWmSi4MMXm3ubPhnGtqCCU9l2DW+apR4D3udY8z3w0WotjZaCTeWzz6vrfiFw7OZSjV4jnjeaoSrIs0PE+6dPODlAYLaiHttfw+eI16iNoKy7MUZ3rJX7G+gq02fNd+rpkUb9S095/pyLVTKqOcwo2Wn3eL88ndrZ16z5FS/yYwDMVplWbPfeDdRGHhRXj/0a/WheGAaE6QmJI4NTnQnPkxfFscdc6QN1dSGIEVOMEzl2nP3i9PlhnwK4TTbVBnRX1oP/xD9GYJ+mjgIH6obIN33Zw/bmai+5d65XNR3tqdHF9RDHveBxlfSagA0vRUdOt9XWwQgGjta7rJTA1MHodKvsxaUnTryFyuvhvwaqnZt5UcEx/ksH14TuSbqPdHkYJCG+Ck8B79cKss8N79s6ZlbRy6xnNzPq5uh63L294SfTi28qYQvsko4FsYnioOj224ntcpG8KX4N8/RG1UJbqMtoVUQI50dVD/HXydWBp9sbNa/u0tnijdH64VJLRk/3NsviEMusRfL+ARR29tilVHVk91UT5Wvxn9fQZUm0kYNboa+h8a26xWmzxqHB5tlsb2XCdOxT8bmlpwD0fgsUD3Py7S+OXBN6iUVvNp4jK03TcOPqLto1HS1ryyv4rNMuqNT7WtJJ6fP5CQwzvZyzfLTFYEp/zqcgfoWzKM6MHUUOqLW0e9+CyV+VGr9ZcvxyJIk+VVDHA8mg45+6MFrf5dcuNXW7kl5Ok6f2cNNwvpvjaBS4ZyR+JNDHqG5L0jr6I28wt7mtGQnFw151ZiOQ5teYViYXVwd8JJ9XlvYTWD5OUp3e1JeL5UGvY4vfku5eagSprkJc+hxJNzD3/zpIOz1OrVsSGy2X2/fwuROPCGg+qbd688/nTrQzrih5RljFaeutRGUWycfg3dulsu/BoeUZUbjG+tq02daR+W+1b/FH9PpMeY/kZ+3eg5TeFWOFK9ifmVnM099ogWhX2Id+9WUBMZqrnqkmX+tFStuMfQwfRJrt59FaNoC73G0/3FJU0fL2mrhj3CvN496ch5atC6YAsZU6NR+d0qHy2izKeqqLrzbqbweyj8eXZ0vub+i5fyKfLxj7N0A4K7qsejQxb5alUivWboV6PiTFf15bPT4OyOPXNXUVfosGNEZTJNvmD1HjZfRk4MfZR0x0kWuuffS/7uxd0Bq+iyYCduwwow71a63b/QgVfVR5SK19Jk7QEMelbMNpz3N0qjTjE1vLGvJ6kvU0meRVeEsLRmtk4LtgjgRWyxyb1l81kvC+v94LKarK2MK9/LVWBsjcvUU2YnPOHLqCtNnOnUxzXUVregwVuXljZTyeBK4VnqH64TJ1KO0YidauHXTqOlmX61LQeg5vdtQSloXOyn1cKj65thHXntNGKeoNEyyLB946F2D1ZpmxnycyHeYjseVHM7kq7FiSiaVoslH7eGZVmK3e1JCZ+kzFW6ME1XLcBNJjMojj44xUs+ZzLe0ajuHtgfPV4s7qkdITKehzaX27RsuRwYga8tVzfXFGu/aq9aJNSz3sDYUIKmJrW5IZ3f1W/aebtq6S8tPfGZ4g/YPGvqKXpq0oKy7pnrCj3a16TPVeaq97LhqycS+KvnSaMXwy08u7GS0GCobvv/a9n/8Vy2Hqp63yHs0LVQE4tPW6JMWJ8wXlnYXqrJY69oWnKbx6ZbbVSH97m+H9mXdG96IDRr/ujwqf9u3bClVAB3rsejQzb5aDUb24XJzajnDEACtyttOB4QE8tgmDsRf3E/feNzvjsJ47x9AECmMn6gykloyQuWVoqesolI48+JJuVDBc6Oc8RuXPcgiDy1lbXhBy06evH/hjt8cnKq635fWFLR8tFnC1yHrrFqr4Gx3ZuGeE+le7EYydPq/Y96sRxWs3G9dSiihIdFpGyPad0+RZ21a9u6aqsVpNXUtfWa8qtrNcDk/MeAyqIa3+QdrY4+V3TRqumsQaWkR2e3FzakN+AWyhTH10GqqaQHuDa8uH1pT3rLDl/HqyZilhrjKgT7miKNdBGNJkrHOEC2mRDrM0PtV7qKdQtsnFdCCjuXsDORoIevKdfI70juYy8xvpDe6Q77fWtg2D8Sw0NiWG1qa1YZVaToNbS75qrHfSdAB0PxqeozTf8XmLbmJrY1X+KXkXzHzo1ZJfqK50NAesI1c2WK1aUFZd031hB/t5qbPjF+hLPGTK1XGV9WPynao6nmLV6x4ocI4gCXMy5TFBxGoQf7x8iiQeheqxL/6R+SjqlzxnT0OXRatBF/ZFH8Lax28Diq7WS4XgDugx6JDN/s6tCqR+5Tb+1/j4249z3LJ6TPjkbvUYgWRPlOC/Bf02axru97EZwvT0edDiVFJ/lPGCyUbM+ZJ31SNOTz3f3Jd2aNlTKzfkUnC15FSZw3T/p2lP9Qohr15OZm97ai8NVW9HW19MBNOX12xNtsYYnbUxzPRtm5iW+iaqsWpNfUvpSV34mRt4YgQep+hJ+kN7yylSsAeK7tp1HTbINK7FLg9kb2xONYJy3xJqYcnG3/Xk0efydSP6cmNXm4Ez4FUdz/t2VLIVaTPZJFu6ActnvrcG94O9mn7pCQt6GSuQamy0drlX43q1TqYJ8SFxJAhXTQOK9PBWkyRzTS5W8ehzZH8qhy2lbyv9Req7uGh1fcy1q9tT78IZg+01sQENV4ht/RX03/U6v6HjfzE9EOyVTO6lhqUNddUT/jRenLwpj68JZpxT24qJBeFKdFCfuX2kkg9b1l4NVE0FY5x7vN24zhqVbJb58hMvwvVRNrqpLSRoU4x/aQ0W6plGGreIdVJwT7Tp7ZWd/aV5gDcYj0WHbrZ16NViUqvxvKxqScFrcqbNHhT1YeMm36QyUfukpd64d2hP7lveJPz/afUwr9/qexNP5cL15iikjwY04VS391EJXoM7lZ7F9RcwxXx5Nqwp9PBm/pwqsh6nal1Vl1CS0YM5LHcOeoArLdWwukn1/UzXDdNq15bninKJdV6OX2WTCsiWAocPUQvbdoYvKkXbpH1Ortp1HTfINK6FDyeWHkWnhXLKJ56KKqVYdLW+lDXYWU9Gjvc7Z/ogFNVx7BmdjLWCpJfDRdT6Tk7XdsnpXSQPlM9zROipEqxqXJeTrGa0Pss04LX/urSwbNGucU6LXYT2i7l1U5yVZ/9byQ9bGnjFUZXxn8OTQkVl1oV0ULeVTWxSZ/FXG367PInh+6qkXMJ0SL4ebhbdBVOM/mhiZtxNussd+HnxaBszW18FH/T03xqSxtq3qGEokTRnga3UWkAcGv1WHToZl9BTn7vbJFVODXyg5zNXhE5b51+rhysL27IJcD97dnU9vrOcb2lzUacfOQOvfWVshlj30XDXbZ8aaB/7vHoRrG89Zt4s+lbkwdjulB66zF5azt9dvlLB6TXWXWJLRmvf4H5ztEWDLXeDAmnn1zXz3Ld/j1vHm967Zl7T0rTiwfzwfHc2PSZHAybMo4MuGaXvnRAV42abvaV5GIj8VU4TUztBTnU0dkSE3D6qjvJm/+Pqynk7WV4xlpB8quhYipLka5p96Q0iUHHLFN7Tbvf1HULJi2N7RgMgTIuHWDXqkdGdEYGk3YT2i7lVes9k3QN/ZnHs3zv2qis2CqcUelVEVWrvKrQ3E5QvqZ6wo92tekzlbF2NvOwERN1d8YXbkhuKiQXhZcSLZyqcPDrylI5czte6T08Q1vl9+CfMs+Am+EutBagzQOtm4Da7g2XL3cKf9Ui0vN3EVpx3EalAcDt1VvRoZt9pdbBq2DFYeuDdH0e6EhXAqNgKRhnS1n0wFatUcVvu7WZxubbkjuHmlNuzxwEXZgTo5I8GNOFUt+d7VDNkuu7PrUAgvM24ygYE1VXjj+2Ta+zapJbMu6tbr5ztAOwXpOE00+u6ydft4t6ufzSyzTdG/bWFfXIs76Z6bOTv4K5Vk0DgoAfSnuQHCzqkoEsVOMdrrtp1HSzr+az7B6VYXZjFQL0D9Wf9yf8I+o6pBySoAKfvQxPvgiJ4cwSebMU6Zp2T0pzrekz1WEqtMKjnGs/mMarLaF1pcP92roJbZfyqvWeSbyGXj0kW9j6Vl75//x/J2eYJFeXXhVRd9FVhWbSZzFXnD7TM+uZ8yZqPi/DeEBzgRVILgovI1q4q5bk5u6Jsj513QB3eV3jws8+rculcYX19LtQ3dOmsZPnzeP1/Eq0o6xh8bXOZSrBteKY9BkAV09Fh272DXwpFXP9c2J2D2uVSNXPLMV+hHq/+SolH7lDPYRvZx1xf+4zZ6/+pWJo6f3EqCQPxnShVMLU/LjIJrm+K+ijeq21zAite0JsQbEMdVZNBy0Zj5b1s9ZKE04/ua6fsKOY+8zZRha/6n3S5VnfuPRZ83D7f17hkLtfnDZPRw38WGmrXphoz5li6+F206i5jAbRv62jNw/n7gUr7Kd2KbClHtQKmH3hpTx1KvmYbYSa1u3aWoZnrBUkvxoqptrsfdbuSWk6CDqJqZ+AvCbR++1s/603lWdu4U35pNlsVNc2nnpt4fjKrQ6vr2XaSbWqk/IXEbpcHYc2x6W8ar1nMl3DdI33z9UPJ2HdAEd6VUTeilcWmtsJytdUT/jRrjp9pqZFnOt7YVqUPU7OLhxf9NeR3FRILgq7jxbuhNDuKOXG2pIo639e/dteOniLBiTOvqFV301ZtvS7UHX+itX+da16dX4smNcw4xj1jKxL4GtUcXypHw3gBuul6NDNvj73EXrxz9rF/kzQ2rFML6LCR6aVklR9rsP0mdZmy1z8ygE1+cmdyC6JUUkejOlCqQxXlm53SnJ9N6B10/tpJtM/r61/bZg/Ib3OqmmvJeMmjkW7QkVw662VcPrJdX3rjvIW/Wkqsqy/OusblD5zxwF5fe3dmV7LJ202QoHro00EnOXZiUOLkvEn3900arpvEHk9ktxsV3PvpSh7s87iFPtQNRuUs5n7bqt0WMbnQD9m7jM1H7R5hZywtk9KaSPouBUnt9BWw4cTQrDK6MXns/9+lJcfOteXyz8dLa/XLMuzeNcntYajbonQVHEdhjbPpbx6Oekzd1lV06V219lw64dq5PKzDDPhZkiftTGPcFtIn8VcefpMr6EuZCkdVNvjZ1MnXkuBJSQXhd1GC3dqm+DpSlM+YbA+KvEfLKe0E9SaaKZOYal3oXrQIcOD80szj/Hxf7Hem+NLIHdOa7RY22b2ZfIB3Fk9FB262dflLucUVPpTq0RqeqaEFVcCqj7XafpMdXHKmFdy/k3xvRgOLzFQyoMxXiitXp6t6ehLru8qqmmay/IkXwVx49Gm11k17aTPzryvI3ibWnnTdswJp59c17ftKFf9jnVmSTnra6oWt1FTb9aK3nPBwRf2VhzQO7QKc8LAFEXOOWV4ktFdo6bbBpG3KEow1jJjl4Kk1IPXkU28mjP23VZ9zzM8dnJozStbf+eMtYLkV8PFVJvzuLd7UkrmoPN9dyovcmFJqTFBHX/krFvVN0NuP6mj9JPy+NcnbbSTPItbMXgzwqvpxd9W234m73D1A7dO/JdaFem0X38bSJ/FXH36zM86+dc9N5rWxaC+G4yBt4yLTG4qJBeF3UULb1Vmrc2WeqjNvZFoiWCgNSAjUyc60u5C2XlNW/XWXUzAugiuWIs6YcW3Dsjn58YFRh3qt52xjwmAO6FnokM3+3pPzvXu9+4Yf//95maAXmeaG5hKro6q+lyn6TMtyiTklVqfXj/f2PUCh6oqGc43MVDKgzFfKNWTIqHpeJRf+l/pq3aYyfVdnVyTfm4gNfKmfUdXlT7ze1CqqULdGR78fV+uGcN2wumr9TdMa/JYVuiXN7Ppet6c9NnF51JpMOdP3KYPLgZ6mliK19msFXXJHeHl/xbM4yK7adR01yDyelvrgTjtUD0pqQctLBonQVMPSBKSMrWtX8d2/ZiqNa8sD2ySL4J88G/sSG6LvNqgUWuvt9bB5PDq394htXtSUtagI/rs++9RIdiyYJ3KOYbjkfj7o7fH4g+pguuTPC1a0H6MtH87Cm1CxgRZ8quXkD4TQ5IjnRDdQWx6/8rvH+UqnJYhnGlVEXnMpgz7JSF9FnMd6TPndjl+F/wSElcm/vpn6tu6aeR0Ey3cAijaZlOrcD7c+Ggp6NMXmtRnho6mn+QhzY2XDc1FeUPrl8v/o6VPnPgHs7QH2qB6bpu6kLQ+jSe8CuAu65Ho0MW+biUpmoLR0oLPd411rM9rJdlUGCklnLiqz3WcPhNPQb23Wdo2p+tjTiATfc3UEI9cNBo6bb/gsE2BUh6M5cG7muMmesWE88OyU9cPV9yT67thzU+vRBcGS47V1zxwqrDJb7ua9FnQyNRuJHfKPH9fc8eN2qzonmA4ffnsKt6vsKm+qfCO2uIV0VPzavbml1zXVC1WFzM0nEfTOi6OFvr6i9M7KekHoPecVfPiJ58459GFHC9ifVs3jZpu9vUCSjSU1IJ/MDy7vC419aANbjWFKtWasFUDjv94oj+KU9HT2CZSqUzj8ahEWDz7piok8cirusmbw9xJ8bn2nbZ9UkK2oCMzTeI96rmR8ZTlXRHN5al81uPR1en8nrYIXu1z87TZjH3jsjKQK747tMViUTOJn10noU1IrjBkfNUa97Omz2RvytDt4fY9j/6ctVU4jalG+Ynmh45B0jn9CXQX0oOyIouOhaSFdG+860mfOVrH88FUtU+nDuI37Xm98lrUEQu/rVnbEq3yiv+PmPsoyo6pxhEx8lXjgEo1DVnsiZBXI8+NfYoVvtY2ybfDLb+jRHr6LLxk2Lx5kmanYbPyIXxU58FqNZGPlnd5fDZHP1QkBuwOqTbVQ/FEJSBjTNLXCuDu6oHo0PG+flFvqNak14mdWuDGYPCewYlKsLSl7qJeXpXTvRkH3accuaStbjYwur3fVIHmvFlzkxF6RV+bY06LIxf1tdXB/sKAqOqZnudrqZkB8xlpc9x4MxBrb/n+pexORRxrNcnhLdlmh2l+UnfLYi1+jM3DLbEqQm7h94qtWq/icpYJ41SVuq/wx6H4Y5xsD4dr3m5ryt89N7S6HiyC6TivH7wJ6hjm6r5af6MwuSNHL57uL5YGn5ReiusQaQmoDgi551ty8jP/VzbwUDRaTCOJrid95g9u9TZjPrF1NB2cV5YtS/YTuF5n1fySv8hM7omx+2RjfULMVpy0hmzHjRpH5/s2P0w48cLwgEQlv8wLaKrchH1aG9WqcrbBqYPIh2vPb9zagh5iWn5pGfnoUODb0zpwnf6zWBrIzYkCOfagyNNcDmomervJ+6DCb6NBQiGeRtHLqP6l+cqpXMnEibZubSfcoGv7pDxa0LFNsa1FW+0gZRiK1UyCSKRXBgL6QRq33FCpeKiNoFfP0px/sPB6Lb6cy+nHmQXnSHJDG4a7pZPQ5kuuMMhXDXMXaNlGW+DQ7uG+0nr8sAXtyqv0mZ83N/QjSX6sqH/R05Eai3ziZf7FXZa0oKzTao8JE7rdfNeWPnM5LYGNZ2Ixr4WXE+Ximpu6Xl8sj3vTvjp/T+6H32rWZD8F54fxqnTc0L/F1okTb4JX8y8Xa/qr56FX5355W/3c0j/odD94HORsj6cOgldPq2vimJ2K5n4ov37xrdlYlpOneKXe5+a3xmF1+e2SbBQ5pcavM9vrO8f10Me5zluN6k6lOKO92X//27392pl37F5tNbfw6wsvjvYXJxerbpq/fryZd4ctOO9/9vYo3NIQ6bMH991X7w2vFsvHdW2X3JMP+1fzG2tWyurLfes+mljOr/zXOwznyGcrzEsCwOZHRocO922d7Abl8MBEJRxQvjebR0GnIWdz8zjxEOAKJm9y3+ZOxLs6X/Kf6O7NT5Se+uWnU54/Kc2uHccfLcaPvB5/CBw4r+35SxP6W+5+4dFQ4ZFX93Iqvn+FKm2hiZz9dw70+29TayO4abh65LGzNqGYvoUaGKcf/QW8gm3AOYyhwgNzRDv7vLYia+255x92a6eRkGfiNo38ryZ3vzg+s7XsPSdfzq++DNpmg2Pb/8SblML3RkU8AHO3hyvLh42mKW62mif7zj87Ezod9wZ2gr56OO9sleJb56PFnexu0VznyV/a2j7qq3FaHeWjxIEqoW/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- "text/plain": [ - "" - ] - }, - "execution_count": 35, - "metadata": { - "image/png": { - "width": 600 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"images/onnxops.png\", width=600)" - ] - }, - { - "cell_type": "markdown", - "id": "1c0f9bdb", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Second API**\n", - "\n", - "Easier to write and read: ``X @ Y.T``" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "89e1da40", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('images/op.png')" - ] - }, - { - "cell_type": "markdown", - "id": "f4d46f8b", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "source": [ - "**Second API: still difficulties**\n", - "\n", - "* Developers need to implement python/numpy code with ONNX operators.\n", - "* Developers need to write code to convert model in several opsets.\n", - "* ONNX is strongly typed, python is not." - ] - }, - { - "cell_type": "markdown", - "id": "fceae273", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Experimentation: numpy API for ONNX\n", - "\n", - "Let the developper use onnx without knowning it.\n", - "\n", - "* Implement a method onnx_transform using numpy.\n", - "* Use the numpy API to convert that code into ONNX.\n", - "* Create a method transform into the class which executes this ONNX with onnxruntime." - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "8105a571", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": { - "image/png": { - "width": 800 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image('images/numpyapi.png', width=800)" - ] - }, - { - "cell_type": "markdown", - "id": "bba9091a", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Third API: less difficulties**\n", - "\n", - "* ONNX is strongly typed, python is not.\n", - "* The API does not cover all cases such as loops or tests.\n", - "* Error might be hard to understand when the syntax is incorrect.\n", - "\n", - "However:\n", - "\n", - "* This API is much easier to use.\n", - "* It reduces dicrepancies as method transform is using ONNX = no discrepancies introduced after conversion since it is the same graph." - ] - }, - { - "cell_type": "markdown", - "id": "abaf57a7", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Example with RFFT" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "id": "1a5d8023", - "metadata": {}, - "outputs": [], - "source": [ - "from typing import Any\n", - "import mlprodict.npy.numpy_onnx_impl as npnx\n", - "from mlprodict.npy import onnxnumpy_np\n", - "from mlprodict.npy.onnx_numpy_annotation import NDArrayType\n", - "\n", - "def dft_real_cst(N, fft_length):\n", - " n = numpy.arange(N)\n", - " k = n.reshape((N, 1)).astype(numpy.float64)\n", - " M = numpy.exp(-2j * numpy.pi * k * n / fft_length)\n", - " both = numpy.empty((2,) + M.shape)\n", - " both[0, :, :] = numpy.real(M)\n", - " both[1, :, :] = numpy.imag(M)\n", - " return both\n", - "\n", - "@onnxnumpy_np(signature=NDArrayType((\"T:all\", ), dtypes_out=('T',)))\n", - "def onnx_rfft(x, fft_length=None):\n", - " if fft_length is None:\n", - " raise RuntimeError(\"fft_length must be specified.\")\n", - "\n", - " size = fft_length // 2 + 1\n", - " cst = dft_real_cst(fft_length, fft_length).astype(numpy.float32)\n", - " xt = npnx.transpose(x, (1, 0))\n", - " res = npnx.matmul(cst[:, :, :fft_length], xt[:fft_length])[:, :size, :]\n", - " return npnx.transpose(res, (0, 2, 1))" - ] - }, - { - "cell_type": "markdown", - "id": "0de80986", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Results are the same." - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "2ed8437c", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[[-1.7784990e+00, 1.4675100e-01, 2.8697007e+00],\n", - " [ 6.3172030e-01, -2.1366143e-01, -3.2235985e+00],\n", - " [ 5.8288980e-01, -1.9905260e+00, -1.1469542e+00]],\n", - "\n", - " [[ 0.0000000e+00, 1.6233883e+00, 4.1927758e-16],\n", - " [ 0.0000000e+00, 3.6831067e+00, -1.0557316e-15],\n", - " [ 0.0000000e+00, 4.6142429e-01, -5.9122794e-17]]], dtype=float32)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "rnd = numpy.random.randn(3, 4).astype(numpy.float32)\n", - "fft_onx = onnx_rfft(rnd, fft_length=rnd.shape[1])\n", - "fft_onx" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "id": "34c78277", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[-1.77849905+0.j , 0.146751 +1.62338826j,\n", - " 2.86970057+0.j ],\n", - " [ 0.63172024+0.j , -0.21366143+3.6831066j ,\n", - " -3.22359842+0.j ],\n", - " [ 0.5828898 +0.j , -1.99052602+0.46142429j,\n", - " -1.14695418+0.j ]])" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fft_np = numpy.fft.rfft(rnd)\n", - "fft_np" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "26374c9a", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "key = list(onnx_rfft.signed_compiled)[0]\n", - "onx = onnx_rfft.signed_compiled[key].compiled.onnx_\n", - "%onnxview onx --size=\"8%\"" - ] - }, - { - "cell_type": "markdown", - "id": "534400d4", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Three API and subestimators\n", - "\n", - "scikit-learn may build model on the top of other models like VotingRegressor: it trains several predictors of any type and merges the answers.\n", - "\n", - "**First API**\n", - "\n", - "```\n", - " vars_names = []\n", - " for i, estimator in enumerate(op.estimators_):\n", - " if estimator is None:\n", - " continue\n", - "\n", - " op_type = sklearn_operator_name_map[type(estimator)]\n", - "\n", - " this_operator = scope.declare_local_operator(op_type, estimator)\n", - " this_operator.inputs = inputs\n", - "\n", - " var_name = scope.declare_local_variable(\n", - " 'var_%d' % i, inputs[0].type.__class__())\n", - " this_operator.outputs.append(var_name)\n", - " var_name = var_name.onnx_name\n", - "\n", - " if op.weights is not None:\n", - " val = op.weights[i] / op.weights.sum()\n", - " else:\n", - " val = 1. / len(op.estimators_)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "ffd0972a", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Second API**\n", - "\n", - "```\n", - " y_list = [OnnxSubEstimator(sub, inp, op_version=op_version,\n", - " options=options)\n", - " for sub in op.estimators_]\n", - "\n", - " # labels\n", - " label_list = [OnnxReshapeApi13(y[0], np.array([-1, 1], dtype=np.int64),\n", - " op_version=op_version)\n", - " for y in y_list]\n", - "\n", - " label = OnnxConcat(*label_list, axis=1, op_version=op_version,\n", - " output_names=[operator.outputs[0]])\n", - " label.add_to(scope=scope, container=container)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "f368aa43", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Third API**\n", - "\n", - "```\n", - " def onnx_graph(self, X):\n", - " h = self.hyperplan_.astype(X.dtype)\n", - " c = self.centers_.astype(X.dtype)\n", - "\n", - " sign = ((X - c[0]) @ h) >= numpy.array([0], dtype=X.dtype)\n", - " cast = sign.astype(X.dtype).reshape((-1, 1))\n", - "\n", - " prob0 = nxnpskl.logistic_regression(X, model=self.lr0_)[1]\n", - " prob1 = nxnpskl.logistic_regression(X, model=self.lr1_)[1]\n", - "\n", - " prob = prob1 * cast - prob0 * (cast - numpy.array([1], dtype=X.dtype))\n", - " label = nxnp.argmax(prob, axis=1)\n", - " return MultiOnnxVar(label, prob)\n", - " ```" - ] - }, - { - "cell_type": "markdown", - "id": "08a5b5f2", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Part 3: challenges and tools\n", - "\n", - "* Challenges\n", - " * Missing converter: [FunctionTransformer](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.FunctionTransformer.html), custom code?\n", - " * Sparse\n", - " * Text\n", - "* Tools\n", - " * Python Runtime for ONNX\n", - " * Tools to check discrepancies\n", - " * Benchmarks !" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "b7157467", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
run previous cell, wait for 2 seconds
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "add_notebook_menu(first_level=2, keep_item=2)" - ] - }, - { - "cell_type": "markdown", - "id": "0413a40f", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Missing converter for custom code\n", - "\n", - "This is a the user code. How to convert that?" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "70373db6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0. , 0.69314718],\n", - " [1.09861229, 1.38629436]])" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "from sklearn.preprocessing import FunctionTransformer\n", - "transformer = FunctionTransformer(np.log1p)\n", - "X = np.array([[0, 1], [2, 3]], dtype=float)\n", - "transformer.transform(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "38153f1d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "FunctionTransformer is not supported unless the transform function is None (= identity). You may raise an issue at https://github.com/onnx/sklearn-onnx/issues.\n" - ] - } - ], - "source": [ - "from skl2onnx import to_onnx\n", - "try:\n", - " to_onnx(transformer, X)\n", - "except Exception as e:\n", - " print(e)" - ] - }, - { - "cell_type": "markdown", - "id": "c552f659", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Currently, the user would have to:\n", - "\n", - "* Create a custom class wrapping the FunctionTransformer.\n", - "* Implement a shape_calculator, a converter attached to it.\n", - "* Register the converter to skl2onnx.\n", - "* Convert the pipeline.\n", - "\n", - "**Sounds quite complex for a simple function ``log1p``.**" - ] - }, - { - "cell_type": "markdown", - "id": "210dd8db", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "But we could use a syntax close to numpy:" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "id": "08805e2d", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0. , 0.69314718],\n", - " [1.09861229, 1.38629436]])" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import mlprodict.npy.numpy_onnx_pyrt as npnxrt\n", - "\n", - "transformer = FunctionTransformer(npnxrt.log1p)\n", - "X = np.array([[0, 1], [2, 3]], dtype=float)\n", - "transformer.transform(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "f9e92021", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlprodict.onnx_conv import to_onnx as to_onnx_extended\n", - "onx = to_onnx_extended(transformer, X, rewrite_ops=True)\n", - "\n", - "%onnxview onx --size=\"8%\"" - ] - }, - { - "cell_type": "markdown", - "id": "513f110a", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Sparse\n", - "\n", - "Two cases:\n", - "\n", - "* Categories\n", - "* Text (Tfidf)\n", - "\n", - "Both preprocessings are producing many sparse columns.\n", - "Sparse becomes dense after conversion. **Two issues:**\n", - "\n", - "* Processing time: much slower\n", - "* Confusion between nan and 0: what is a missing value in a sparse matrix? (xgboost and lightgbm disagree)" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "id": "a4c6759d", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "<100x100 sparse matrix of type ''\n", - "\twith 100 stored elements in Compressed Sparse Row format>" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.preprocessing import OneHotEncoder\n", - "X = numpy.arange(100).reshape((-1, 1))\n", - "ohe = OneHotEncoder(sparse=True).fit(X)\n", - "ohe.transform(X)" - ] - }, - { - "cell_type": "code", - "execution_count": 48, - "id": "051da97a", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "onx = to_onnx(ohe, X)\n", - "%onnxview onx --size=\"8%\"" - ] - }, - { - "cell_type": "markdown", - "id": "7302ab46", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Text\n", - "\n", - "\n", - "* Text processing uses **sparse** most of the time.\n", - "* They are often custom: heavily dependent on how the text was produced (lower, upper, remove special characters, ...)\n", - "* They use embeddings: glove, word2text, deep learning, ...\n", - "\n", - "What to do?\n", - "\n", - "* [ONNX Zoo](https://github.com/onnx/models/tree/master/text/machine_comprehension)\n", - "* New operators for common tokenizers [onnxruntime-extensions/tokenizer](https://github.com/microsoft/onnxruntime-extensions/tree/main/operators/tokenizer)\n", - "* Simple text processing [onnxruntime-extensions/text](https://github.com/microsoft/onnxruntime-extensions/tree/main/operators/text)\n", - "\n", - "Back to main problem? How do you write a custom text preprocessing which can be easily converted into ONNX?" - ] - }, - { - "cell_type": "markdown", - "id": "4a25ff67", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Tools\n", - "\n", - "Developpers usually do not need tool to write converter.\n", - "\n", - "They need tools to check the conversion worked fine.\n", - "\n", - "* A tool to check discrepancies.\n", - "* A tool to check processing time.\n", - "\n", - "They would like to access benchmarks too.\n", - "\n", - "**Next:** tools to be agile when implementing converters" - ] - }, - { - "cell_type": "markdown", - "id": "f7921c9f", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### python runtime for ONNX" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "5cebed82", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.preprocessing import OneHotEncoder\n", - "X = numpy.arange(100).reshape((-1, 1))\n", - "ohe = OneHotEncoder(sparse=True).fit(X)\n", - "ohe.transform(X)\n", - "onx = to_onnx(ohe, X)" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "id": "c53a5013", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'variable': array([[1., 0., 0., ..., 0., 0., 0.],\n", - " [0., 1., 0., ..., 0., 0., 0.],\n", - " [0., 0., 1., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 1., 0., 0.],\n", - " [0., 0., 0., ..., 0., 1., 0.],\n", - " [0., 0., 0., ..., 0., 0., 1.]], dtype=float32)}" - ] - }, - "execution_count": 50, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlprodict.onnxrt import OnnxInference\n", - "oinf = OnnxInference(onx)\n", - "oinf.run({'X': X})" - ] - }, - { - "cell_type": "markdown", - "id": "40b2c73d", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Look into intermediate results." - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "id": "33036e18", - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-- OnnxInference: run 5 nodes\n", - "Onnx-ArrayFeatureExtractor(X, X0) -> X01 (name='ArrayFeatureExtractor')\n", - "+kr='X01': (100, 1) (dtype=int32 min=0 max=99)\n", - "Onnx-Cast(X01) -> X01cast (name='Cast')\n", - "+kr='X01cast': (100, 1) (dtype=int64 min=0 max=99)\n", - "Onnx-OneHotEncoder(X01cast) -> X01out (name='OneHotEncoder')\n", - "+kr='X01out': (100, 1, 100) (dtype=float32 min=0.0 max=1.0)\n", - "Onnx-Concat(X01out) -> concat_result (name='Concat')\n", - "+kr='concat_result': (100, 1, 100) (dtype=float32 min=0.0 max=1.0)\n", - "Onnx-Reshape(concat_result, shape_tensor) -> variable (name='Reshape')\n", - "+kr='variable': (100, 100) (dtype=float32 min=0.0 max=1.0)\n" - ] - }, - { - "data": { - "text/plain": [ - "{'variable': array([[1., 0., 0., ..., 0., 0., 0.],\n", - " [0., 1., 0., ..., 0., 0., 0.],\n", - " [0., 0., 1., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 1., 0., 0.],\n", - " [0., 0., 0., ..., 0., 1., 0.],\n", - " [0., 0., 0., ..., 0., 0., 1.]], dtype=float32)}" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oinf.run({'X': X}, verbose=1, fLOG=print)" - ] - }, - { - "cell_type": "markdown", - "id": "fbf1f985", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Look into intermediate results." - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "id": "55609f25", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-kv='X' shape=(100, 1) dtype=int32 min=0 max=99\n", - "-kv='X0' shape=() dtype=int64 min=0 max=0\n", - "-kv='shape_tensor' shape=(2,) dtype=int64 min=-1 max=100\n", - "-- OnnxInference: run 5 nodes\n", - "Onnx-ArrayFeatureExtractor(X, X0) -> X01 (name='ArrayFeatureExtractor')\n", - "+kr='X01': (100, 1) (dtype=int32 min=0 max=99)\n", - "[[ 0]\n", - " [ 1]\n", - " [ 2]\n", - " ...\n", - " [97]\n", - " [98]\n", - " [99]]\n", - "Onnx-Cast(X01) -> X01cast (name='Cast')\n", - "+kr='X01cast': (100, 1) (dtype=int64 min=0 max=99)\n", - "[[ 0]\n", - " [ 1]\n", - " [ 2]\n", - " ...\n", - " [97]\n", - " [98]\n", - " [99]]\n", - "Onnx-OneHotEncoder(X01cast) -> X01out (name='OneHotEncoder')\n", - "+kr='X01out': (100, 1, 100) (dtype=float32 min=0.0 max=1.0)\n", - "[[[1. 0. 0. ... 0. 0. 0.]]\n", - "\n", - " [[0. 1. 0. ... 0. 0. 0.]]\n", - "\n", - " [[0. 0. 1. ... 0. 0. 0.]]\n", - "\n", - " ...\n", - "\n", - " [[0. 0. 0. ... 1. 0. 0.]]\n", - "\n", - " [[0. 0. 0. ... 0. 1. 0.]]\n", - "\n", - " [[0. 0. 0. ... 0. 0. 1.]]]\n", - "Onnx-Concat(X01out) -> concat_result (name='Concat')\n", - "+kr='concat_result': (100, 1, 100) (dtype=float32 min=0.0 max=1.0)\n", - "[[[1. 0. 0. ... 0. 0. 0.]]\n", - "\n", - " [[0. 1. 0. ... 0. 0. 0.]]\n", - "\n", - " [[0. 0. 1. ... 0. 0. 0.]]\n", - "\n", - " ...\n", - "\n", - " [[0. 0. 0. ... 1. 0. 0.]]\n", - "\n", - " [[0. 0. 0. ... 0. 1. 0.]]\n", - "\n", - " [[0. 0. 0. ... 0. 0. 1.]]]\n", - "Onnx-Reshape(concat_result, shape_tensor) -> variable (name='Reshape')\n", - "+kr='variable': (100, 100) (dtype=float32 min=0.0 max=1.0)\n", - "[[1. 0. 0. ... 0. 0. 0.]\n", - " [0. 1. 0. ... 0. 0. 0.]\n", - " [0. 0. 1. ... 0. 0. 0.]\n", - " ...\n", - " [0. 0. 0. ... 1. 0. 0.]\n", - " [0. 0. 0. ... 0. 1. 0.]\n", - " [0. 0. 0. ... 0. 0. 1.]]\n" - ] - }, - { - "data": { - "text/plain": [ - "{'variable': array([[1., 0., 0., ..., 0., 0., 0.],\n", - " [0., 1., 0., ..., 0., 0., 0.],\n", - " [0., 0., 1., ..., 0., 0., 0.],\n", - " ...,\n", - " [0., 0., 0., ..., 1., 0., 0.],\n", - " [0., 0., 0., ..., 0., 1., 0.],\n", - " [0., 0., 0., ..., 0., 0., 1.]], dtype=float32)}" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "oinf.run({'X': X}, verbose=10, fLOG=print)" - ] - }, - { - "cell_type": "markdown", - "id": "9a874903", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Intermediate results inserted into ONNX graph" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "f2df9a61", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "new_onx = oinf.run2onnx({'X': X})\n", - "%onnxview new_onx[1] --size=\"8%\"" - ] - }, - { - "cell_type": "markdown", - "id": "334e7a94", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Plot a TreeEnsembleRegressor" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "id": "48541a59", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "n_targets=1\n", - "n_trees=1\n", - "----\n", - "treeid=0\n", - " X2 <= 2.4499998\n", - " F X2 <= 4.95\n", - " F X3 <= 1.75\n", - " F y=2.0 f=0 i=14\n", - " T X3 <= 1.55\n", - " F X2 <= 5.45\n", - " F y=2.0 f=0 i=13\n", - " T y=1.0 f=0 i=12\n", - " T y=2.0 f=0 i=10\n", - " T X3 <= 1.65\n", - " F X1 <= 3.1\n", - " F y=1.0 f=0 i=7\n", - " T y=2.0 f=0 i=6\n", - " T y=1.0 f=0 i=4\n", - " T y=0.0 f=0 i=1\n" - ] - } - ], - "source": [ - "from sklearn.tree import DecisionTreeRegressor\n", - "from sklearn.datasets import load_iris\n", - "from sklearn.model_selection import train_test_split\n", - "from mlprodict.plotting.text_plot import onnx_text_plot_tree\n", - "data = load_iris()\n", - "X, y = data.data.astype(numpy.float32), data.target\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", - "model = DecisionTreeRegressor().fit(X_train, y_train)\n", - "onx = to_onnx(model, X)\n", - "\n", - "print(onnx_text_plot_tree(onx.graph.node[0]))" - ] - }, - { - "cell_type": "markdown", - "id": "659bd531", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Export ONNX to numpy code\n", - "\n", - "It does not support all operators yet." - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "1fe74edf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.datasets import load_iris\n", - "from sklearn.model_selection import train_test_split\n", - "data = load_iris()\n", - "X, y = data.data, data.target\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", - "model = LogisticRegression().fit(X_train, y_train)\n", - "onx = to_onnx(model, X, options={'zipmap': False})\n", - "\n", - "%onnxview onx --size=\"8%\"" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "c741148e", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "import numpy\n", - "import scipy.special as scipy_special\n", - "import scipy.spatial.distance as scipy_distance\n", - "from mlprodict.onnx_tools.exports.numpy_helper import (\n", - " argmax_use_numpy_select_last_index,\n", - " argmin_use_numpy_select_last_index,\n", - " array_feature_extrator,\n", - " make_slice)\n", - "\n", - "\n", - "def numpy_ONNX_LogisticRegression(X):\n", - " '''\n", - " Numpy function for ``ONNX_LogisticRegression``.\n", - "\n", - " * producer: skl2onnx\n", - " * version: 0\n", - " * description: \n", - " '''\n", - " # initializers\n", - "\n", - " list_value = [-0.3820146698920944, 0.3978447755662121, -0.0158301056741248, 0.8896867906595531, -0.14568789790854544, -0.74399889275101, -\n", - " 2.362597473810776, -0.08723543846877874, 2.449832912279553, -0.9319091952800573, -0.8514850826078488, 1.7833942778879046]\n", - " coef = numpy.array(list_value, dtype=numpy.float64).reshape((4, 3))\n", - "\n", - " intercept = numpy.array([9.111545808820148, 1.675404528603912, -\n", - " 10.786950337424146], dtype=numpy.float64).reshape((1, 3))\n", - "\n", - " classes = numpy.array([0, 1, 2], dtype=numpy.int32)\n", - "\n", - " # nodes\n", - "\n", - " multiplied = X @ coef\n", - " raw_scores = multiplied + intercept\n", - " label1 = numpy.expand_dims(numpy.argmax(raw_scores, axis=1), -1)\n", - " probabilities = scipy_special.softmax(raw_scores, axis=-1)\n", - " array_feature_extractor_result = array_feature_extrator(classes, label1)\n", - " cast2_result = array_feature_extractor_result.astype(numpy.float64)\n", - " reshaped_result = cast2_result.reshape(-1)\n", - " label = reshaped_result.astype(numpy.int64)\n", - "\n", - " return label, probabilities\n", - "\n" - ] - } - ], - "source": [ - "from mlprodict.onnx_tools.onnx_export import export2numpy\n", - "print(export2numpy(onx))" - ] - }, - { - "cell_type": "markdown", - "id": "34ec437b", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Export ONNX to tf2onnx code\n", - "\n", - "It does not support all operators yet." - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "4b99627b", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "import inspect\n", - "import collections\n", - "import numpy\n", - "from onnx import AttributeProto, TensorProto\n", - "from onnx.helper import (\n", - " make_model, make_node, set_model_props, make_tensor, make_graph,\n", - " make_tensor_value_info)\n", - "# from tf2onnx.utils import make_name, make_sure, map_onnx_to_numpy_type\n", - "from mlprodict.onnx_tools.exports.tf2onnx_helper import (\n", - " make_name, make_sure, map_onnx_to_numpy_type)\n", - "# from tf2onnx.handler import tf_op\n", - "# from tf2onnx.graph_builder import GraphBuilder\n", - "from mlprodict.onnx_tools.exports.tf2onnx_helper import (\n", - " tf_op, Tf2OnnxConvert, GraphBuilder)\n", - "\n", - "\n", - "@tf_op(\"ONNX_LogisticRegression\")\n", - "class ConvertONNX_LogisticRegressionOp:\n", - "\n", - " supported_dtypes = [\n", - " numpy.float32,\n", - " ]\n", - "\n", - " @classmethod\n", - " def any_version(cls, opset, ctx, node, **kwargs):\n", - " '''\n", - " Converter for ``ONNX_LogisticRegression``.\n", - "\n", - " * producer: skl2onnx\n", - " * version: 0\n", - " * description: \n", - " '''\n", - " oldnode = node\n", - " input_name = node.input[0]\n", - " onnx_dtype = ctx.get_dtype(input_name)\n", - " np_dtype = map_onnx_to_numpy_type(onnx_dtype)\n", - " make_sure(np_dtype in ConvertONNX_LogisticRegressionOp.supported_dtypes,\n", - " \"Unsupported input type.\")\n", - " shape = ctx.get_shape(input_name)\n", - " varx = {x: x for x in node.input}\n", - "\n", - " # initializers\n", - " if getattr(ctx, 'verbose', False):\n", - " print('[initializers] %r' % cls)\n", - "\n", - " list_value = [-0.3820146698920944, 0.3978447755662121, -0.0158301056741248, 0.8896867906595531, -0.14568789790854544, -0.74399889275101, -\n", - " 2.362597473810776, -0.08723543846877874, 2.449832912279553, -0.9319091952800573, -0.8514850826078488, 1.7833942778879046]\n", - " value = numpy.array(list_value, dtype=numpy.float64).reshape((4, 3))\n", - " varx['coef'] = ctx.make_const(\n", - " name=make_name('init_coef'), np_val=value).name\n", - "\n", - " value = numpy.array([9.111545808820148, 1.675404528603912, -\n", - " 10.786950337424146], dtype=numpy.float64).reshape((1, 3))\n", - " varx['intercept'] = ctx.make_const(\n", - " name=make_name('init_intercept'), np_val=value).name\n", - "\n", - " value = numpy.array([0, 1, 2], dtype=numpy.int32)\n", - " varx['classes'] = ctx.make_const(\n", - " name=make_name('init_classes'), np_val=value).name\n", - "\n", - " value = numpy.array([-1], dtype=numpy.int64)\n", - " varx['shape_tensor'] = ctx.make_const(\n", - " name=make_name('init_shape_tensor'), np_val=value).name\n", - "\n", - " # nodes\n", - " if getattr(ctx, 'verbose', False):\n", - " print('[nodes] %r' % cls)\n", - "\n", - " inputs = [varx['X'], varx['coef']]\n", - " node = ctx.make_node('MatMul', inputs=inputs, name=make_name('MatMul'))\n", - " varx['multiplied'] = node.output[0]\n", - "\n", - " inputs = [varx['multiplied'], varx['intercept']]\n", - " node = ctx.make_node('Add', inputs=inputs, name=make_name('Add'))\n", - " varx['raw_scores'] = node.output[0]\n", - "\n", - " inputs = [varx['raw_scores']]\n", - " node = ctx.make_node('ArgMax', inputs=inputs, attr=dict(\n", - " axis=1), name=make_name('ArgMax'))\n", - " varx['label1'] = node.output[0]\n", - "\n", - " inputs = [varx['raw_scores']]\n", - " node = ctx.make_node('Softmax', inputs=inputs, attr=dict(\n", - " axis=-1), name=make_name('Softmax'))\n", - " varx['probabilities'] = node.output[0]\n", - "\n", - " inputs = [varx['classes'], varx['label1']]\n", - " node = ctx.make_node('ArrayFeatureExtractor', inputs=inputs,\n", - " domain='ai.onnx.ml', name=make_name('ArrayFeatureExtractor'))\n", - " varx['array_feature_extractor_result'] = node.output[0]\n", - "\n", - " inputs = [varx['array_feature_extractor_result']]\n", - " node = ctx.make_node('Cast', inputs=inputs, attr=dict(\n", - " to=TensorProto.DOUBLE), name=make_name('Cast'))\n", - " varx['cast2_result'] = node.output[0]\n", - "\n", - " inputs = [varx['cast2_result'], varx['shape_tensor']]\n", - " node = ctx.make_node('Reshape', inputs=inputs,\n", - " name=make_name('Reshape'))\n", - " varx['reshaped_result'] = node.output[0]\n", - "\n", - " inputs = [varx['reshaped_result']]\n", - " node = ctx.make_node('Cast', inputs=inputs, attr=dict(\n", - " to=TensorProto.INT64), name=make_name('Cast1'))\n", - " varx['label'] = node.output[0]\n", - "\n", - " # finalize\n", - " if getattr(ctx, 'verbose', False):\n", - " print('[replace_all_inputs] %r' % cls)\n", - " ctx.replace_all_inputs(oldnode.output[0], node.output[0])\n", - " ctx.remove_node(oldnode.name)\n", - "\n", - " @classmethod\n", - " def version_13(cls, ctx, node, **kwargs):\n", - " return cls.any_version(13, ctx, node, **kwargs)\n", - "\n", - "\n", - "def create_model():\n", - " inputs = []\n", - " outputs = []\n", - "\n", - " # inputs\n", - " print('[inputs]') # verbose\n", - "\n", - " value = make_tensor_value_info('X', 11, (, dim_value: 4\n", - " ))\n", - " inputs.append(value)\n", - "\n", - " # outputs\n", - " print('[outputs]') # verbose\n", - "\n", - " value = make_tensor_value_info('label', 7, (,))\n", - " outputs.append(value)\n", - "\n", - " value = make_tensor_value_info('probabilities', 11, (, dim_value: 3\n", - " ))\n", - " outputs.append(value)\n", - "\n", - " inames = [i.name for i in inputs]\n", - " onames = [i.name for i in outputs]\n", - " node = make_node('ONNX_LogisticRegression', inames,\n", - " onames, name='ONNX_LogisticRegression')\n", - "\n", - " # graph\n", - " print('[graph]') # verbose\n", - " graph = make_graph([node], 'ONNX_LogisticRegression', inputs, outputs)\n", - " onnx_model = make_model(graph)\n", - " onnx_model.ir_version = 7\n", - " onnx_model.producer_name = 'skl2onnx'\n", - " onnx_model.producer_version = ''\n", - " onnx_model.domain = 'ai.onnx'\n", - " onnx_model.model_version = 0\n", - " onnx_model.doc_string = ''\n", - " set_model_props(onnx_model, {})\n", - "\n", - " # opsets\n", - " print('[opset]') # verbose\n", - " opsets = {'': 13, 'ai.onnx.ml': 13}\n", - " del onnx_model.opset_import[:] # pylint: disable=E1101\n", - " for dom, value in opsets.items():\n", - " op_set = onnx_model.opset_import.add()\n", - " op_set.domain = dom\n", - " op_set.version = value\n", - "\n", - " return onnx_model\n", - "\n", - "\n", - "onnx_raw = create_model()\n", - "onnx_model = Tf2OnnxConvert(onnx_raw, tf_op, target_opset={\n", - " '': 13, 'ai.onnx.ml': 13}).run()\n", - "\n" - ] - } - ], - "source": [ - "from mlprodict.onnx_tools.onnx_export import export2tf2onnx\n", - "print(export2tf2onnx(onx))" - ] - }, - { - "cell_type": "markdown", - "id": "cb0b636f", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Export to ONNX" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "c5718c9c", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "import numpy\n", - "from onnx import numpy_helper, TensorProto\n", - "from onnx.helper import (\n", - " make_model, make_node, set_model_props, make_tensor, make_graph,\n", - " make_tensor_value_info)\n", - "\n", - "\n", - "def create_model():\n", - " '''\n", - " Converted ``ONNX_LogisticRegression``.\n", - "\n", - " * producer: skl2onnx\n", - " * version: 0\n", - " * description: \n", - " '''\n", - " # containers\n", - " print('[containers]') # verbose\n", - " initializers = []\n", - " nodes = []\n", - " inputs = []\n", - " outputs = []\n", - "\n", - " # opsets\n", - " print('[opsets]') # verbose\n", - " opsets = {'': 13, 'ai.onnx.ml': 13}\n", - " target_opset = 13\n", - "\n", - " # initializers\n", - " print('[initializers]') # verbose\n", - "\n", - " list_value = [-0.3820146698920944, 0.3978447755662121, -0.0158301056741248, 0.8896867906595531, -0.14568789790854544, -0.74399889275101, -\n", - " 2.362597473810776, -0.08723543846877874, 2.449832912279553, -0.9319091952800573, -0.8514850826078488, 1.7833942778879046]\n", - " value = numpy.array(list_value, dtype=numpy.float64).reshape((4, 3))\n", - "\n", - " tensor = numpy_helper.from_array(value, name='coef')\n", - " initializers.append(tensor)\n", - "\n", - " list_value = [9.111545808820148, 1.675404528603912, -10.786950337424146]\n", - " value = numpy.array(list_value, dtype=numpy.float64).reshape((1, 3))\n", - "\n", - " tensor = numpy_helper.from_array(value, name='intercept')\n", - " initializers.append(tensor)\n", - "\n", - " list_value = [0, 1, 2]\n", - " value = numpy.array(list_value, dtype=numpy.int32)\n", - "\n", - " tensor = numpy_helper.from_array(value, name='classes')\n", - " initializers.append(tensor)\n", - "\n", - " list_value = [-1]\n", - " value = numpy.array(list_value, dtype=numpy.int64)\n", - "\n", - " tensor = numpy_helper.from_array(value, name='shape_tensor')\n", - " initializers.append(tensor)\n", - "\n", - " # inputs\n", - " print('[inputs]') # verbose\n", - "\n", - " value = make_tensor_value_info('X', 11, (, dim_value: 4\n", - " ))\n", - " inputs.append(value)\n", - "\n", - " # outputs\n", - " print('[outputs]') # verbose\n", - "\n", - " value = make_tensor_value_info('label', 7, (,))\n", - " outputs.append(value)\n", - "\n", - " value = make_tensor_value_info('probabilities', 11, (, dim_value: 3\n", - " ))\n", - " outputs.append(value)\n", - "\n", - " # nodes\n", - " print('[nodes]') # verbose\n", - "\n", - " node = make_node(\n", - " 'MatMul',\n", - " ['X', 'coef'],\n", - " ['multiplied'],\n", - " name='MatMul', domain='')\n", - " nodes.append(node)\n", - "\n", - " node = make_node(\n", - " 'Add',\n", - " ['multiplied', 'intercept'],\n", - " ['raw_scores'],\n", - " name='Add', domain='')\n", - " nodes.append(node)\n", - "\n", - " node = make_node(\n", - " 'ArgMax',\n", - " ['raw_scores'],\n", - " ['label1'],\n", - " name='ArgMax', axis=1, domain='')\n", - " nodes.append(node)\n", - "\n", - " node = make_node(\n", - " 'Softmax',\n", - " ['raw_scores'],\n", - " ['probabilities'],\n", - " name='Softmax', axis=-1, domain='')\n", - " nodes.append(node)\n", - "\n", - " node = make_node(\n", - " 'ArrayFeatureExtractor',\n", - " ['classes', 'label1'],\n", - " ['array_feature_extractor_result'],\n", - " name='ArrayFeatureExtractor', domain='ai.onnx.ml')\n", - " nodes.append(node)\n", - "\n", - " node = make_node(\n", - " 'Cast',\n", - " ['array_feature_extractor_result'],\n", - " ['cast2_result'],\n", - " name='Cast', to=TensorProto.DOUBLE, domain='')\n", - " nodes.append(node)\n", - "\n", - " node = make_node(\n", - " 'Reshape',\n", - " ['cast2_result', 'shape_tensor'],\n", - " ['reshaped_result'],\n", - " name='Reshape', domain='')\n", - " nodes.append(node)\n", - "\n", - " node = make_node(\n", - " 'Cast',\n", - " ['reshaped_result'],\n", - " ['label'],\n", - " name='Cast1', to=TensorProto.INT64, domain='')\n", - " nodes.append(node)\n", - "\n", - " # graph\n", - " print('[graph]') # verbose\n", - " graph = make_graph(nodes, 'ONNX_LogisticRegression',\n", - " inputs, outputs, initializers)\n", - " onnx_model = make_model(graph)\n", - " onnx_model.ir_version = 7\n", - " onnx_model.producer_name = 'skl2onnx'\n", - " onnx_model.producer_version = ''\n", - " onnx_model.domain = 'ai.onnx'\n", - " onnx_model.model_version = 0\n", - " onnx_model.doc_string = ''\n", - " set_model_props(onnx_model, {})\n", - "\n", - " # opsets\n", - " print('[opset]') # verbose\n", - " del onnx_model.opset_import[:] # pylint: disable=E1101\n", - " for dom, value in opsets.items():\n", - " op_set = onnx_model.opset_import.add()\n", - " op_set.domain = dom\n", - " op_set.version = value\n", - "\n", - " return onnx_model\n", - "\n", - "\n", - "onnx_model = create_model()\n", - "\n" - ] - } - ], - "source": [ - "from mlprodict.onnx_tools.onnx_export import export2onnx\n", - "print(export2onnx(onx))" - ] - }, - { - "cell_type": "markdown", - "id": "7a745b68", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Plots: compares two implementations" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "id": "e21a52a0", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "from mlprodict.plotting.plotting_benchmark import plot_benchmark_metrics\n", - "\n", - "data = {(1, 1): 0.1, (10, 1): 1, (1, 10): 2,\n", - " (10, 10): 100, (100, 1): 100, (100, 10): 1000}\n", - "\n", - "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", - "plot_benchmark_metrics(data, ax=ax[0], cbar_kw={'shrink': 0.6})\n", - "plot_benchmark_metrics(data, ax=ax[1], transpose=True,\n", - " xlabel='X', ylabel='Y',\n", - " cbarlabel=\"ratio\");" - ] - }, - { - "cell_type": "markdown", - "id": "4dd8fa41", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Plots: compare runtimes" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "f4c184a3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "time_kwargs={1: {'number': 100, 'repeat': 100}, 10: {'number': 50, 'repeat': 50}, 100: {'number': 40, 'repeat': 50}, 1000: {'number': 40, 'repeat': 40}, 10000: {'number': 20, 'repeat': 20}}\n", - "[enumerate_validated_operator_opsets] opset in [14, None].\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "LinearRegression : 0%| | 0/2 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from mlprodict.cli import validate_runtime\n", - "\n", - "validate_runtime(\n", - " verbose=1,\n", - " out_raw=\"data.csv\", out_summary=\"summary.csv\",\n", - " benchmark=True, dump_folder=\"dump_errors\",\n", - " runtime=['python', 'onnxruntime1'],\n", - " models=['LinearRegression', 'DecisionTreeRegressor'],\n", - " n_features=[4, 10], dtype=\"32\",\n", - " out_graph=\"bench.png\",\n", - " time_kwargs={\n", - " 1: {\"number\": 100, \"repeat\": 100}, 10: {\"number\": 50, \"repeat\": 50},\n", - " 100: {\"number\": 40, \"repeat\": 50}, 1000: {\"number\": 40, \"repeat\": 40},\n", - " 10000: {\"number\": 20, \"repeat\": 20}})" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "3980406b", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Image('bench.png')" - ] - }, - { - "cell_type": "markdown", - "id": "948d5b68", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Modifies ONNX graph" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "id": "9aa6a72d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from mlprodict.onnx_tools.onnx_manipulations import select_model_inputs_outputs\n", - "\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.datasets import load_iris\n", - "from sklearn.model_selection import train_test_split\n", - "data = load_iris()\n", - "X, y = data.data, data.target\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", - "model = LogisticRegression().fit(X_train, y_train)\n", - "onx = to_onnx(model, X, options={'zipmap': False})\n", - "\n", - "%onnxview onx --size=\"8%\"" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "id": "5f16de8f", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "simple_onx = select_model_inputs_outputs(onx, outputs=['raw_scores'], infer_shapes=True)\n", - "%onnxview simple_onx --size=\"8%\"" - ] - }, - { - "cell_type": "markdown", - "id": "1131d6fd", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Merges two ONNX: OnnxTransformer" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "id": "cb3797e0", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from pyensae.datasource import download_data\n", - "\n", - "src = (\"https://s3.amazonaws.com/onnx-model-zoo/mobilenet/\"\n", - " \"mobilenetv2-1.0/\")\n", - "model_file = \"mobilenetv2-1.0.onnx\"\n", - "src = \"https://s3.amazonaws.com/onnx-model-zoo/squeezenet/squeezenet1.1/\"\n", - "model_file = \"squeezenet1.1.onnx\"\n", - "\n", - "if not os.path.exists(model_file):\n", - " print(\"Download '{0}'...\".format(model_file))\n", - " download_data(model_file, website=src)\n", - " print(\"Done.\")" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "b4cd532d", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "((1, 3, 224, 224), 0.0, 0.9843137)" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy\n", - "from PIL import Image as PILImage\n", - "\n", - "\n", - "def preprocess(img):\n", - " img2 = img.resize((224, 224))\n", - " X = numpy.asarray(img2).transpose((2, 0, 1))\n", - " X = X[numpy.newaxis, :3, :, :] / 255.0\n", - " return X.astype(numpy.float32)\n", - "\n", - "url = \"https://github.com/sdpython/mlprodict/raw/master/_doc/notebooks/\"\n", - "img_path = \"800px-Tour_Eiffel_Wikimedia_Commons_(cropped).jpg\"\n", - "download_data(img_path, website=url)\n", - "image = PILImage.open(img_path)\n", - "image.reduce(4)\n", - "\n", - "image_data = preprocess(image)\n", - "image_data.shape, image_data.min(), image_data.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "id": "5bc00de2", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.00053482, 0.00035403, 0.00059171, 0.00070281, 0.0005738 ],\n", - " dtype=float32)" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.pipeline import Pipeline\n", - "from sklearn.preprocessing import Normalizer\n", - "from mlprodict.sklapi import OnnxTransformer\n", - "\n", - "with open(model_file, 'rb') as f: content = f.read()\n", - "\n", - "pipe = Pipeline([\n", - " ('squeeze', OnnxTransformer(content, runtime='onnxruntime1')),\n", - " ('scaler', Normalizer(norm='l1'))])\n", - "pipe.fit(image_data)\n", - "onx_pred = pipe.transform(image_data)\n", - "onx_pred[0, :5]" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "id": "912a79b3", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "onx = to_onnx(pipe, image_data)\n", - "with open(\"squeeze_norm.onnx\", \"wb\") as f:\n", - " f.write(onx.SerializeToString())" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "id": "42ae7d35", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 68, - "metadata": { - "image/png": { - "width": 150 - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "Image(\"images/sqsc.png\", width=150)" - ] - }, - { - "cell_type": "markdown", - "id": "667867ac", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Speed up a scikit-learn step" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "id": "9dec70a8", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy\n", - "from numpy.testing import assert_almost_equal\n", - "from pandas import DataFrame\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.datasets import make_regression\n", - "from sklearn.decomposition import PCA\n", - "from pyquickhelper.pycode.profiling import profile\n", - "from mlprodict.sklapi import OnnxSpeedupTransformer\n", - "from mlprodict.tools.speed_measure import measure_time\n", - "\n", - "data, _ = make_regression(1000, n_features=20)\n", - "data = data.astype(numpy.float64)\n", - "model = PCA(n_components=10).fit(data)\n", - "\n", - "model_ort = OnnxSpeedupTransformer(PCA(n_components=10), runtime='onnxruntime1').fit(data)\n", - "\n", - "assert_almost_equal(model.transform(data), model_ort.transform(data), decimal=6)" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "id": "7bc37698", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "147 µs ± 26.6 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" - ] - } - ], - "source": [ - "%timeit model.transform(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "id": "93bc4aa2", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "127 µs ± 11.8 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n" - ] - } - ], - "source": [ - "%timeit model_ort.transform(data)" - ] - }, - { - "cell_type": "markdown", - "id": "600dd6e8", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Other tools\n", - "\n", - "* TreeEnsembleRegressorDouble, SvmRegressorDouble: to investigate discrepancies\n", - "* Custom operators: Solve\n", - "* Benchmark with [asv](https://github.com/airspeed-velocity/asv)\n", - "* Distances between two ONNX graphs (not yet complete)\n", - "* Did you say Einsum? [Einsum Decomposition](http://www.xavierdupre.fr/app/mlprodict/helpsphinx/notebooks/einsum_decomposition.html) or [FFT](http://www.xavierdupre.fr/app/mlprodict/helpsphinx/notebooks/onnx_fft.html#onnxfftrst)\n", - "* OnnxComplexAbs: to check implementation with FFT" - ] - }, - { - "cell_type": "markdown", - "id": "8575dc47", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "**Operator calls done with python script**" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "id": "e3a3daa8", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "def compiled_run(dict_inputs):\n", - " # init: classes (classes)\n", - " # init: coef (coef)\n", - " # init: intercept (intercept)\n", - " # init: shape_tensor (shape_tensor)\n", - " # inputs\n", - " X = dict_inputs['X']\n", - " (multiplied, ) = n0_matmul(X, coef)\n", - " (raw_scores, ) = n1_add(multiplied, intercept)\n", - " (label1, ) = n2_argmax_12(raw_scores)\n", - " (probabilities, ) = n3_softmax(raw_scores)\n", - " (array_feature_extractor_result, ) = n4_arrayfeatureextractor(classes, label1)\n", - " (cast2_result, ) = n5_cast(array_feature_extractor_result)\n", - " (reshaped_result, ) = n6_reshape_13(cast2_result, shape_tensor)\n", - " (label, ) = n7_cast(reshaped_result)\n", - " return {\n", - " 'label': label,\n", - " 'probabilities': probabilities,\n", - " }\n" - ] - } - ], - "source": [ - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.datasets import load_iris\n", - "from sklearn.model_selection import train_test_split\n", - "data = load_iris()\n", - "X, y = data.data, data.target\n", - "X_train, X_test, y_train, y_test = train_test_split(X, y)\n", - "model = LogisticRegression().fit(X_train, y_train)\n", - "onx = to_onnx(model, X, options={'zipmap': False})\n", - "oinf = OnnxInference(onx, runtime=\"python_compiled\")\n", - "print(oinf._run_compiled_code)" - ] - }, - { - "cell_type": "markdown", - "id": "09e5a483", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Questions..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b89f3a81", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "celltoolbar": "Diaporama", - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/requirements.txt b/docs/requirements.txt index 4adc6fdba..401b34eff 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -9,7 +9,6 @@ lightgbm loky matplotlib mlinsights>=0.3.631 -mlprodict>=0.4.1259 nbsphinx onnx onnxruntime diff --git a/docs/tutorial/plot_abegin_convert_pipeline.py b/docs/tutorial/plot_abegin_convert_pipeline.py index 2cf8f72db..665f9fabb 100644 --- a/docs/tutorial/plot_abegin_convert_pipeline.py +++ b/docs/tutorial/plot_abegin_convert_pipeline.py @@ -28,7 +28,7 @@ from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from skl2onnx import to_onnx -from mlprodict.onnxrt import OnnxInference +from onxn.reference import ReferenceEvaluator X, y = load_diabetes(return_X_y=True) @@ -106,15 +106,14 @@ def diff(p1, p2): # the prediction. It is not meant to be used into # production (it still relies on python), but it is # useful to investigate why the conversion went wrong. -# It uses module :epkg:`mlprodict`. -oinf = OnnxInference(onx, runtime="python_compiled") +oinf = ReferenceEvaluator(onx) print(oinf) ########################################## # It works almost the same way. -pred_pyrt = oinf.run({'X': X_test.astype(numpy.float32)})['variable'] +pred_pyrt = oinf.run(None, {'X': X_test.astype(numpy.float32)})[0] print(diff(pred_skl, pred_pyrt)) ############################# diff --git a/docs/tutorial/plot_bbegin_measure_time.py b/docs/tutorial/plot_bbegin_measure_time.py index d40b743f3..fa1eb6073 100644 --- a/docs/tutorial/plot_bbegin_measure_time.py +++ b/docs/tutorial/plot_bbegin_measure_time.py @@ -17,6 +17,7 @@ import numpy from pandas import DataFrame from tqdm import tqdm +from onnx.reference import ReferenceEvaluator from sklearn import config_context from sklearn.datasets import make_regression from sklearn.ensemble import ( @@ -24,7 +25,6 @@ VotingRegressor) from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split -from mlprodict.onnxrt import OnnxInference from onnxruntime import InferenceSession from skl2onnx import to_onnx from skl2onnx.tutorial import measure_time @@ -86,7 +86,7 @@ onx = to_onnx(ereg, X_train[:1].astype(numpy.float32), target_opset=14) sess = InferenceSession(onx.SerializeToString()) -oinf = OnnxInference(onx, runtime="python_compiled") +oinf = ReferenceEvaluator(onx) obs = [] for batch_size, repeat in tqdm(sizes): @@ -106,7 +106,7 @@ number=10, repeat=repeat) mt['ort'] = mt2['average'] / mt['size'] - # mlprodict + # ReferenceEvaluator context = {"oinf": oinf, 'X': X_test[:batch_size].astype(numpy.float32)} mt2 = measure_time( "oinf.run({'X': X})['variable']", context, div_by_number=True, diff --git a/docs/tutorial/plot_dbegin_options.py b/docs/tutorial/plot_dbegin_options.py index 7695014da..d884f4bce 100644 --- a/docs/tutorial/plot_dbegin_options.py +++ b/docs/tutorial/plot_dbegin_options.py @@ -23,15 +23,13 @@ probabilites are stored in dictionaries. That's the purpose of operator *ZipMap* added at the end of the following graph. -.. gdot:: - :script: DOT-SECTION - +.. runpython:: import numpy + from onnx.helper import printable_graph from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from skl2onnx import to_onnx - from mlprodict.onnxrt import OnnxInference iris = load_iris() X, y = iris.data, iris.target @@ -40,22 +38,19 @@ clr.fit(X_train, y_train) model_def = to_onnx(clr, X_train.astype(numpy.float32)) - oinf = OnnxInference(model_def) - print("DOT-SECTION", oinf.to_dot()) + print(printable_graph(model_def)) This operator is not really efficient as it copies every probabilies and labels in a different container. This time is usually significant for small classifiers. Then it makes sense to remove it. -.. gdot:: - :script: DOT-SECTION - +.. runpython:: import numpy + from onnx.helper import printable_graph from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from skl2onnx import to_onnx - from mlprodict.onnxrt import OnnxInference iris = load_iris() X, y = iris.data, iris.target @@ -65,8 +60,7 @@ model_def = to_onnx(clr, X_train.astype(numpy.float32), options={LogisticRegression: {'zipmap': False}}) - oinf = OnnxInference(model_def) - print("DOT-SECTION", oinf.to_dot()) + print(printable_graph(model_def)) There might be in the graph many classifiers, it is important to have a way to specify which classifier should keep its *ZipMap* @@ -76,6 +70,7 @@ from pprint import pformat import numpy from pyquickhelper.helpgen.graphviz_helper import plot_graphviz +from onnx.reference import ReferenceEvaluator from sklearn.ensemble import RandomForestClassifier from sklearn.preprocessing import MinMaxScaler from sklearn.pipeline import Pipeline @@ -85,7 +80,6 @@ from skl2onnx.common._registration import _converter_pool from skl2onnx import to_onnx from onnxruntime import InferenceSession -from mlprodict.onnxrt import OnnxInference iris = load_iris() X, y = iris.data, iris.target @@ -95,7 +89,7 @@ model_def = to_onnx(clr, X_train.astype(numpy.float32), options={id(clr): {'zipmap': False}}) -oinf = OnnxInference(model_def, runtime='python_compiled') +oinf = ReferenceEvaluator(model_def) print(oinf) ################################## @@ -111,16 +105,9 @@ # what it would give with operator *ZipMap*. model_def = to_onnx(clr, X_train.astype(numpy.float32)) -oinf = OnnxInference(model_def, runtime='python_compiled') +oinf = ReferenceEvaluator(model_def) print(oinf) -################################## -# Visually. - -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) - ####################################### # Using function *id* has one flaw: it is not pickable. @@ -128,18 +115,10 @@ model_def = to_onnx(clr, X_train.astype(numpy.float32), options={'zipmap': False}) -oinf = OnnxInference(model_def, runtime='python_compiled') +oinf = ReferenceEvaluator(model_def) print(oinf) -################################## -# Visually. - -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) - - ####################################### # Option in a pipeline # ++++++++++++++++++++ @@ -156,17 +135,9 @@ model_def = to_onnx(pipe, X_train.astype(numpy.float32), options={'clr__zipmap': False}) -oinf = OnnxInference(model_def, runtime='python_compiled') +oinf = ReferenceEvaluator(model_def) print(oinf) -################################## -# Visually. - -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) - - ####################################### # Option *raw_scores* # +++++++++++++++++++ @@ -187,8 +158,8 @@ pipe, X_train.astype(numpy.float32), options={id(pipe): {'zipmap': False}}) -oinf = OnnxInference(model_def, runtime='python_compiled') -print(oinf.run({'X': X.astype(numpy.float32)[:5]})) +oinf = ReferenceEvaluator(model_def) +print(oinf.run(None, {'X': X.astype(numpy.float32)[:5]})) ####################################### @@ -198,8 +169,8 @@ pipe, X_train.astype(numpy.float32), options={id(pipe): {'raw_scores': True, 'zipmap': False}}) -oinf = OnnxInference(model_def, runtime='python_compiled') -print(oinf.run({'X': X.astype(numpy.float32)[:5]})) +oinf = ReferenceEvaluator(model_def) +print(oinf.run(None, {'X': X.astype(numpy.float32)[:5]})) ######################################### # It did not seem to work... We need to tell @@ -210,8 +181,8 @@ pipe, X_train.astype(numpy.float32), options={id(pipe.steps[1][1]): {'raw_scores': True, 'zipmap': False}}) -oinf = OnnxInference(model_def, runtime='python_compiled') -print(oinf.run({'X': X.astype(numpy.float32)[:5]})) +oinf = ReferenceEvaluator(model_def) +print(oinf.run(None, {'X': X.astype(numpy.float32)[:5]})) ########################################### # There are negative values. That works. @@ -221,8 +192,8 @@ pipe, X_train.astype(numpy.float32), options={'clr__raw_scores': True, 'clr__zipmap': False}) -oinf = OnnxInference(model_def, runtime='python_compiled') -print(oinf.run({'X': X.astype(numpy.float32)[:5]})) +oinf = ReferenceEvaluator(model_def) +print(oinf.run(None, {'X': X.astype(numpy.float32)[:5]})) ######################################### diff --git a/docs/tutorial/plot_dbegin_options_list.py b/docs/tutorial/plot_dbegin_options_list.py index d7c0ab1b1..40ded3812 100644 --- a/docs/tutorial/plot_dbegin_options_list.py +++ b/docs/tutorial/plot_dbegin_options_list.py @@ -20,7 +20,7 @@ of operators is for model *GaussianMixture*. """ from pyquickhelper.helpgen.graphviz_helper import plot_graphviz -from mlprodict.onnxrt import OnnxInference +from onnx.reference import ReferenceEvaluator from timeit import timeit import numpy from onnxruntime import InferenceSession @@ -49,15 +49,6 @@ print(sess.run(None, {'X': xt})[2]) -################################## -# Display the ONNX graph. - - -oinf = OnnxInference(model_onnx) -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) - ################################### # Conversion without ReduceLogSumExp # ++++++++++++++++++++++++++++++++++ @@ -77,15 +68,6 @@ print(model.score_samples(xt)) print(sess2.run(None, {'X': xt})[2]) -################################## -# Display the ONNX graph. - -oinf = OnnxInference(model_onnx2) -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) - - ####################################### # Processing time # +++++++++++++++ diff --git a/docs/tutorial/plot_ebegin_float_double.py b/docs/tutorial/plot_ebegin_float_double.py index 78b354df8..2cf99f7bd 100644 --- a/docs/tutorial/plot_ebegin_float_double.py +++ b/docs/tutorial/plot_ebegin_float_double.py @@ -49,7 +49,6 @@ different results is not null. The following graph shows the discord areas. """ -from mlprodict.sklapi import OnnxPipeline from skl2onnx.sklapi import CastTransformer from skl2onnx import to_onnx from onnxruntime import InferenceSession diff --git a/docs/tutorial/plot_fbegin_investigate.py b/docs/tutorial/plot_fbegin_investigate.py index dd2aadf5d..80a62b7f0 100644 --- a/docs/tutorial/plot_fbegin_investigate.py +++ b/docs/tutorial/plot_fbegin_investigate.py @@ -28,8 +28,8 @@ then the pipeline with steps 1, 2, then 1, 2, 3... """ from pyquickhelper.helpgen.graphviz_helper import plot_graphviz -from mlprodict.onnxrt import OnnxInference import numpy +from onnx.reference import ReferenceEvaluator from onnxruntime import InferenceSession from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler @@ -102,9 +102,8 @@ onx = to_onnx(pipe, X[:1].astype(numpy.float32), target_opset=17) -oinf = OnnxInference(onx) -oinf.run({'X': X[:2].astype(numpy.float32)}, - verbose=1, fLOG=print) +oinf = ReferenceEvaluator(onx) +oinf.run(None, {'X': X[:2].astype(numpy.float32)}, verbose=1) ################################### # And to get a sense of the intermediate results. diff --git a/docs/tutorial/plot_gbegin_dataframe.py b/docs/tutorial/plot_gbegin_dataframe.py index 33ecc6b59..739a056ba 100644 --- a/docs/tutorial/plot_gbegin_dataframe.py +++ b/docs/tutorial/plot_gbegin_dataframe.py @@ -18,11 +18,9 @@ from mlinsights.plotting import pipeline2dot import numpy import pprint -from mlprodict.onnx_conv import guess_schema_from_data +from onnx.reference import ReferenceEvaluator from onnxruntime import InferenceSession from pyquickhelper.helpgen.graphviz_helper import plot_graphviz -from mlprodict.onnxrt import OnnxInference -from mlprodict.onnx_conv import to_onnx as to_onnx_ext from skl2onnx import to_onnx from pandas import DataFrame from sklearn.pipeline import Pipeline @@ -82,16 +80,6 @@ pipe, train_data[:1], options={RandomForestClassifier: {'zipmap': False}}) -####################################### -# Graph -# +++++ - - -oinf = OnnxInference(onx) -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) - ################################# # Prediction with ONNX @@ -109,8 +97,8 @@ ########################### # Let's use a shortcut -oinf = OnnxInference(onx) -got = oinf.run(train_data) +oinf = ReferenceEvaluator(onx) +got = oinf.run(None, train_data) print(pipe.predict(train_data)) print(got['label']) diff --git a/docs/tutorial/plot_gbegin_transfer_learning.py b/docs/tutorial/plot_gbegin_transfer_learning.py index c2f0c799e..415d6e266 100644 --- a/docs/tutorial/plot_gbegin_transfer_learning.py +++ b/docs/tutorial/plot_gbegin_transfer_learning.py @@ -28,7 +28,6 @@ import sys from io import BytesIO import onnx -from mlprodict.sklapi import OnnxTransformer from sklearn.decomposition import PCA from sklearn.pipeline import Pipeline from mlinsights.plotting.gallery import plot_gallery_images diff --git a/docs/tutorial/plot_gexternal_catboost.py b/docs/tutorial/plot_gexternal_catboost.py index ab5796b4c..5cf1d72a6 100644 --- a/docs/tutorial/plot_gexternal_catboost.py +++ b/docs/tutorial/plot_gexternal_catboost.py @@ -24,7 +24,6 @@ from sklearn.datasets import load_iris from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler -from mlprodict.onnxrt import OnnxInference import onnxruntime as rt from skl2onnx import convert_sklearn, update_registered_converter from skl2onnx.common.shape_calculator import calculate_linear_classifier_output_shapes # noqa @@ -147,12 +146,3 @@ def skl2onnx_convert_catboost(scope, operator, container): pred_onx = sess.run(None, {"input": X[:5].astype(numpy.float32)}) print("predict", pred_onx[0]) print("predict_proba", pred_onx[1][:1]) - -############################# -# Final graph -# +++++++++++ - -oinf = OnnxInference(model_onnx) -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) diff --git a/docs/tutorial/plot_gexternal_lightgbm.py b/docs/tutorial/plot_gexternal_lightgbm.py index f0d6c6546..e11f38afe 100644 --- a/docs/tutorial/plot_gexternal_lightgbm.py +++ b/docs/tutorial/plot_gexternal_lightgbm.py @@ -20,7 +20,6 @@ +++++++++++++++++++++++++++ """ from pyquickhelper.helpgen.graphviz_helper import plot_graphviz -from mlprodict.onnxrt import OnnxInference import onnxruntime as rt from skl2onnx import convert_sklearn, update_registered_converter from skl2onnx.common.shape_calculator import calculate_linear_classifier_output_shapes # noqa @@ -93,13 +92,3 @@ pred_onx = sess.run(None, {"input": X[:5].astype(numpy.float32)}) print("predict", pred_onx[0]) print("predict_proba", pred_onx[1][:1]) - -############################# -# Final graph -# +++++++++++ - - -oinf = OnnxInference(model_onnx) -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) diff --git a/docs/tutorial/plot_gexternal_xgboost.py b/docs/tutorial/plot_gexternal_xgboost.py index fa5814938..3cf467ff5 100644 --- a/docs/tutorial/plot_gexternal_xgboost.py +++ b/docs/tutorial/plot_gexternal_xgboost.py @@ -21,7 +21,6 @@ ++++++++++++++++++++++++++ """ from pyquickhelper.helpgen.graphviz_helper import plot_graphviz -from mlprodict.onnxrt import OnnxInference import numpy import onnxruntime as rt from sklearn.datasets import load_iris, load_diabetes, make_classification @@ -117,17 +116,6 @@ print("predict", pred_onx[0]) print("predict_proba", pred_onx[1][:1]) -############################# -# Final graph -# +++++++++++ - - -oinf = OnnxInference(model_onnx) -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) - - ####################################### # Same example with XGBRegressor # ++++++++++++++++++++++++++++++ diff --git a/docs/tutorial/plot_icustom_converter.py b/docs/tutorial/plot_icustom_converter.py index 57c3f7304..347c7aea0 100644 --- a/docs/tutorial/plot_icustom_converter.py +++ b/docs/tutorial/plot_icustom_converter.py @@ -31,7 +31,6 @@ If *X* is a matrix of features, :math:`V=\\frac{1}{n}X'X` is the covariance matrix. We compute :math:`X V^{1/2}`. """ -from mlprodict.onnxrt import OnnxInference from pyquickhelper.helpgen.graphviz_helper import plot_graphviz import pickle from io import BytesIO @@ -231,12 +230,3 @@ def diff(p1, p2): ############################################# # The differences are smaller with double as expected. - -############################# -# Final graph -# +++++++++++ - -oinf = OnnxInference(onx) -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) diff --git a/docs/tutorial/plot_kcustom_converter_wrapper.py b/docs/tutorial/plot_kcustom_converter_wrapper.py index d484c0094..6a4fd37d8 100644 --- a/docs/tutorial/plot_kcustom_converter_wrapper.py +++ b/docs/tutorial/plot_kcustom_converter_wrapper.py @@ -25,7 +25,6 @@ If *X* is a matrix of features, :math:`V=\\frac{1}{n}X'X` is the covariance matrix. We compute :math:`X V^{1/2}`. """ -from mlprodict.onnxrt import OnnxInference from pyquickhelper.helpgen.graphviz_helper import plot_graphviz import pickle from io import BytesIO @@ -188,13 +187,3 @@ def diff(p1, p2): ############################################# # The differences are smaller with double as expected. - - -############################# -# Final graph -# +++++++++++ - -oinf = OnnxInference(onx) -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) diff --git a/docs/tutorial/plot_lcustom_options.py b/docs/tutorial/plot_lcustom_options.py index a3a538294..e9e0b894d 100644 --- a/docs/tutorial/plot_lcustom_options.py +++ b/docs/tutorial/plot_lcustom_options.py @@ -22,7 +22,6 @@ ++++++++++++ """ -from mlprodict.onnxrt import OnnxInference from pyquickhelper.helpgen.graphviz_helper import plot_graphviz from pandas import DataFrame from skl2onnx.tutorial import measure_time @@ -166,15 +165,6 @@ def diff(p1, p2): print(diff(exp, got2)) -############################## -# Visually. - - -oinf = OnnxInference(onx2) -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) - ######################################### # Time comparison diff --git a/docs/tutorial/plot_mcustom_parser.py b/docs/tutorial/plot_mcustom_parser.py index 0da42a868..49bcd97dc 100644 --- a/docs/tutorial/plot_mcustom_parser.py +++ b/docs/tutorial/plot_mcustom_parser.py @@ -25,7 +25,6 @@ +++++++++++++++++ """ from pyquickhelper.helpgen.graphviz_helper import plot_graphviz -from mlprodict.onnxrt import OnnxInference import numpy from onnxruntime import InferenceSession from sklearn.base import TransformerMixin, BaseEstimator @@ -174,18 +173,3 @@ def diff(p1, p2): print(diff(exp, y1)) print(diff(exp, y2)) - - -################################ -# It works. The final looks like the following. - -oinf = OnnxInference(onx, runtime="python_compiled") -print(oinf) - -############################# -# Final graph -# +++++++++++ - -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) diff --git a/docs/tutorial/plot_pextend_python_runtime.py b/docs/tutorial/plot_pextend_python_runtime.py index 022415e28..2decde3cb 100644 --- a/docs/tutorial/plot_pextend_python_runtime.py +++ b/docs/tutorial/plot_pextend_python_runtime.py @@ -18,7 +18,7 @@ including a new ONNX operator but then it requires a runtime for it to be tested. This example shows how to do that with the python runtime implemented in -:epkg:`mlprodict`. It may not be :epkg:`onnxruntime` +:epkg:`onnx`. It may not be :epkg:`onnxruntime` but that speeds up the implementation of the converter. The example changes the transformer from @@ -33,7 +33,6 @@ training time but at prediction time. """ -from mlprodict.onnxrt.ops_cpu import OpRunCustom, register_operator from skl2onnx.algebra.onnx_ops import ( OnnxAdd, OnnxCast, @@ -49,12 +48,12 @@ OnnxTranspose, ) from skl2onnx.algebra import OnnxOperator -from mlprodict.onnxrt import OnnxInference from pyquickhelper.helpgen.graphviz_helper import plot_graphviz import pickle from io import BytesIO import numpy from numpy.testing import assert_almost_equal +from onnx.reference import ReferenceEvaluator from sklearn.base import TransformerMixin, BaseEstimator from sklearn.datasets import load_iris from skl2onnx.common.data_types import guess_numpy_type, guess_proto_type @@ -360,10 +359,10 @@ def run(self, x, **kwargs): register_operator(OpEig, name='Eig', overwrite=False) -oinf = OnnxInference(onx) +oinf = ReferenceEvaluator(onx) exp = dec.transform(X.astype(numpy.float32)) -got = oinf.run({'X': X.astype(numpy.float32)})['variable'] +got = oinf.run(None, {'X': X.astype(numpy.float32)})[0] def diff(p1, p2): @@ -377,12 +376,3 @@ def diff(p1, p2): ############################################# # It works! - -############################# -# Final graph -# +++++++++++ - -oinf = OnnxInference(onx) -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) diff --git a/tests/test_utils/reference_implementation_svm.py b/tests/test_utils/reference_implementation_svm.py index 2a0e6b22a..8f6d263d5 100644 --- a/tests/test_utils/reference_implementation_svm.py +++ b/tests/test_utils/reference_implementation_svm.py @@ -361,50 +361,3 @@ def _run( for i in labels]), final_scores) return (np.array(labels, dtype=np.int64), final_scores) - - if __name__ == "__main__": - from onnx.reference import ReferenceEvaluator - from onnx.reference.ops.op_argmax import ArgMax_12 as _ArgMax - from sklearn.datasets import make_regression, make_classification - from sklearn.svm import SVR, SVC - from skl2onnx import to_onnx - from reference_implementation_afe import ArrayFeatureExtractor - - class ArgMax(_ArgMax): - def _run(self, data, axis=None, keepdims=None, - select_last_index=None): - if select_last_index == 0: # type: ignore - return _ArgMax._run( - self, data, axis=axis, keepdims=keepdims) - raise NotImplementedError("Unused in sklearn-onnx.") - - # classification 1 - X, y = make_classification( - 100, n_features=6, n_classes=4, n_informative=3, n_redundant=0) - y[:50] = 0 - y[50:] = 1 - model = SVC(probability=False).fit(X, y) - onx = to_onnx(model, X.astype(np.float32), - options={"zipmap": False}) - tr = ReferenceEvaluator( - onx, new_ops=[SVMClassifier, - ArrayFeatureExtractor, ArgMax]) - print("-----------------------") - print(tr.run(None, {"X": X[:4].astype(np.float32)})) - print("--") - from mlprodict.onnxrt import OnnxInference - oinf = OnnxInference(onx) - print(oinf.run({"X": X[:4].astype(np.float32)})) - print("--") - print(model.predict(X[:4].astype(np.float32))) - print(model.decision_function(X[:4].astype(np.float32))) - print(model.predict_proba(X[:4].astype(np.float32))) - print("-----------------------") - - # regression - X, y = make_regression(100, n_features=4) - model = SVR().fit(X, y) - onx = to_onnx(model, X.astype(np.float32)) - tr = ReferenceEvaluator(onx, new_ops=[SVMRegressor]) - print(tr.run(None, {"X": X[:5].astype(np.float32)})) - print(model.predict(X[:5].astype(np.float32))) diff --git a/tests_onnxmltools/test_xgboost_converters.py b/tests_onnxmltools/test_xgboost_converters.py index 6366cb601..4a8201f0a 100644 --- a/tests_onnxmltools/test_xgboost_converters.py +++ b/tests_onnxmltools/test_xgboost_converters.py @@ -224,7 +224,7 @@ def test_model_stacking_classifier_column_transformer_custom(self): X[:, 0] = X[:, 0].astype(np.int64).astype(X.dtype) df['A'] = df.A.astype(np.int64) df['B'] = df.B.astype(np.float32) - df['C'] = df.C.astype(np.str) + df['C'] = df.C.astype(np.str_) y = (iris.target == 0).astype(np.int32) model_to_test.fit(df, y) model_onnx = convert_sklearn( From 7fc573c66d43b4436ad246efaa81847bf0ded6fa Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 14:59:15 +0200 Subject: [PATCH 06/15] remove unused import Signed-off-by: Xavier Dupre --- tests/test_algebra_custom_model_sub_estimator.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tests/test_algebra_custom_model_sub_estimator.py b/tests/test_algebra_custom_model_sub_estimator.py index f3472b394..e304a8409 100644 --- a/tests/test_algebra_custom_model_sub_estimator.py +++ b/tests/test_algebra_custom_model_sub_estimator.py @@ -4,7 +4,6 @@ Tests scikit-learn's binarizer converter. """ import unittest -import logging import warnings import numpy as np from numpy.testing import assert_almost_equal From eebef30ebfc64685bb6df7ce61b5053e48b594fc Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 15:19:05 +0200 Subject: [PATCH 07/15] doc Signed-off-by: Xavier Dupre --- docs/index_tutorial.rst | 1 - docs/tutorial/plot_pextend_python_runtime.py | 378 ------------------- docs/tutorial/plot_qextend_onnxruntime.py | 21 -- docs/tutorial_3_new_operator.rst | 25 -- 4 files changed, 425 deletions(-) delete mode 100644 docs/tutorial/plot_pextend_python_runtime.py delete mode 100644 docs/tutorial/plot_qextend_onnxruntime.py delete mode 100644 docs/tutorial_3_new_operator.rst diff --git a/docs/index_tutorial.rst b/docs/index_tutorial.rst index 1bdcc2af5..524ad0481 100644 --- a/docs/index_tutorial.rst +++ b/docs/index_tutorial.rst @@ -17,7 +17,6 @@ involving operator not actually implemented in tutorial_1_simple tutorial_1-5_external tutorial_2_new_converter - tutorial_3_new_operator tutorial_4_advanced tutorial_2-5_extlib diff --git a/docs/tutorial/plot_pextend_python_runtime.py b/docs/tutorial/plot_pextend_python_runtime.py deleted file mode 100644 index 2decde3cb..000000000 --- a/docs/tutorial/plot_pextend_python_runtime.py +++ /dev/null @@ -1,378 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 - -""" - -.. _l-extend-python-runtime: - -Fast design with a python runtime -================================= - -.. index:: custom python runtime - -:epkg:`ONNX operators` do not contain all operators -from :epkg:`numpy`. There is no operator for -`solve `_ but this one -is needed to implement the prediction function -of model :epkg:`NMF`. The converter can be written -including a new ONNX operator but then it requires a -runtime for it to be tested. This example shows how -to do that with the python runtime implemented in -:epkg:`onnx`. It may not be :epkg:`onnxruntime` -but that speeds up the implementation of the converter. - -The example changes the transformer from -:ref:`l-plot-custom-converter`, the method *predict* -decorrelates the variables by computing the eigen -values. Method *fit* does not do anything anymore. - -A transformer which decorrelates variables -++++++++++++++++++++++++++++++++++++++++++ - -This time, the eigen values are not estimated at -training time but at prediction time. -""" - -from skl2onnx.algebra.onnx_ops import ( - OnnxAdd, - OnnxCast, - OnnxDiv, - OnnxGatherElements, - OnnxEyeLike, - OnnxMatMul, - OnnxMul, - OnnxPow, - OnnxReduceMean_13, - OnnxShape, - OnnxSub, - OnnxTranspose, -) -from skl2onnx.algebra import OnnxOperator -from pyquickhelper.helpgen.graphviz_helper import plot_graphviz -import pickle -from io import BytesIO -import numpy -from numpy.testing import assert_almost_equal -from onnx.reference import ReferenceEvaluator -from sklearn.base import TransformerMixin, BaseEstimator -from sklearn.datasets import load_iris -from skl2onnx.common.data_types import guess_numpy_type, guess_proto_type -from skl2onnx import to_onnx -from skl2onnx import update_registered_converter - - -class LiveDecorrelateTransformer(TransformerMixin, BaseEstimator): - """ - Decorrelates correlated gaussian features. - - :param alpha: avoids non inversible matrices - by adding *alpha* identity matrix - - *Attributes* - - * `self.nf_`: number of expected features - """ - - def __init__(self, alpha=0.): - BaseEstimator.__init__(self) - TransformerMixin.__init__(self) - self.alpha = alpha - - def fit(self, X, y=None, sample_weights=None): - if sample_weights is not None: - raise NotImplementedError( - "sample_weights != None is not implemented.") - self.nf_ = X.shape[1] - return self - - def transform(self, X): - mean_ = numpy.mean(X, axis=0, keepdims=True) - X2 = X - mean_ - V = X2.T @ X2 / X2.shape[0] - if self.alpha != 0: - V += numpy.identity(V.shape[0]) * self.alpha - L, P = numpy.linalg.eig(V) - Linv = L ** (-0.5) - diag = numpy.diag(Linv) - root = P @ diag @ P.transpose() - coef_ = root - return (X - mean_) @ coef_ - - -def test_live_decorrelate_transformer(): - data = load_iris() - X = data.data - - dec = LiveDecorrelateTransformer() - dec.fit(X) - pred = dec.transform(X) - cov = pred.T @ pred - cov /= cov[0, 0] - assert_almost_equal(numpy.identity(4), cov) - - dec = LiveDecorrelateTransformer(alpha=1e-10) - dec.fit(X) - pred = dec.transform(X) - cov = pred.T @ pred - cov /= cov[0, 0] - assert_almost_equal(numpy.identity(4), cov) - - st = BytesIO() - pickle.dump(dec, st) - dec2 = pickle.load(BytesIO(st.getvalue())) - assert_almost_equal(dec.transform(X), dec2.transform(X)) - - -test_live_decorrelate_transformer() - -########################################### -# Everything works as expected. -# -# Extend ONNX -# +++++++++++ -# -# The conversion requires one operator to compute -# the eigen values and vectors. The list of -# :epkg:`ONNX operators` does not contain anything -# which produces eigen values. It does not seem -# efficient to implement an algorithm with existing -# ONNX operators to find eigen values. -# A new operator must be -# added, we give it the same name *Eig* as in :epkg:`numpy`. -# It would take a matrix and would produce one or two outputs, -# the eigen values and the eigen vectors. -# Just for the exercise, a parameter specifies -# to output the eigen vectors as a second output. -# -# New ONNX operator -# ^^^^^^^^^^^^^^^^^ -# -# Any unknown operator can be -# added to an ONNX graph. Operators are grouped by domain, -# `''` or `ai.onnx` refers to matrix computation. -# `ai.onnx.ml` refers to usual machine learning models. -# New domains are officially supported by :epkg:`onnx` package. -# We want to create a new operator `Eig` of domain `onnxcustom`. -# It must be declared in a class, then a converter can use it. - - -class OnnxEig(OnnxOperator): - """ - Defines a custom operator not defined by ONNX - specifications but in onnxruntime. - """ - - since_version = 1 # last changed in this version - expected_inputs = [('X', 'T')] # input names and types - expected_outputs = [('EigenValues', 'T'), # output names and types - ('EigenVectors', 'T')] - input_range = [1, 1] # only one input is allowed - output_range = [1, 2] # 1 or 2 outputs are produced - is_deprecated = False # obviously not deprecated - domain = 'onnxcustom' # domain, anything is ok - operator_name = 'Eig' # operator name - past_version = {} # empty as it is the first version - - def __init__(self, X, eigv=False, op_version=None, **kwargs): - """ - :param X: array or OnnxOperatorMixin - :param eigv: also produces the eigen vectors - :param op_version: opset version - :param kwargs: additional parameters - """ - OnnxOperator.__init__( - self, X, eigv=eigv, op_version=op_version, **kwargs) - - -print(OnnxEig('X', eigv=True)) - -################################## -# Now we can write the converter and -# the shape calculator. -# -# shape calculator -# ^^^^^^^^^^^^^^^^ -# -# Nothing new here. - - -def live_decorrelate_transformer_shape_calculator(operator): - op = operator.raw_operator - input_type = operator.inputs[0].type.__class__ - input_dim = operator.inputs[0].type.shape[0] - output_type = input_type([input_dim, op.nf_]) - operator.outputs[0].type = output_type - - -################################## -# converter -# ^^^^^^^^^ -# -# The converter is using the class `OnnxEig`. The code -# is longer than previous converters as the computation is -# more complex too. - - -def live_decorrelate_transformer_converter(scope, operator, container): - # shortcuts - op = operator.raw_operator - opv = container.target_opset - out = operator.outputs - - # We retrieve the unique input. - X = operator.inputs[0] - - # We guess its type. If the operator ingests float (or double), - # it outputs float (or double). - proto_dtype = guess_proto_type(X.type) - dtype = guess_numpy_type(X.type) - - # Lines in comment specify the numpy computation - # the ONNX code implements. - # mean_ = numpy.mean(X, axis=0, keepdims=True) - mean = OnnxReduceMean_13(X, axes=[0], keepdims=1, op_version=opv) - - # This is trick I often use. The converter automatically - # chooses a name for every output. In big graph, - # it is difficult to know which operator is producing which output. - # This line just tells every node must prefix its ouputs with this string. - # It also applies to all inputs nodes unless this method - # was called for one of these nodes. - mean.set_onnx_name_prefix('mean') - - # X2 = X - mean_ - X2 = OnnxSub(X, mean, op_version=opv) - - # V = X2.T @ X2 / X2.shape[0] - N = OnnxGatherElements( - OnnxShape(X, op_version=opv), - numpy.array([0], dtype=numpy.int64), - op_version=opv) - Nf = OnnxCast(N, to=proto_dtype, op_version=opv) - - # Every output involved in N and Nf is prefixed by 'N'. - Nf.set_onnx_name_prefix('N') - - V = OnnxDiv( - OnnxMatMul(OnnxTranspose(X2, op_version=opv), - X2, op_version=opv), - Nf, op_version=opv) - V.set_onnx_name_prefix('V1') - - # V += numpy.identity(V.shape[0]) * self.alpha - V = OnnxAdd(V, - op.alpha * numpy.identity(op.nf_, dtype=dtype), - op_version=opv) - V.set_onnx_name_prefix('V2') - - # L, P = numpy.linalg.eig(V) - LP = OnnxEig(V, eigv=True, op_version=opv) - LP.set_onnx_name_prefix('LP') - - # Linv = L ** (-0.5) - # Notation LP[0] means OnnxPow is taking the first output - # of operator OnnxEig, LP[1] would mean the second one - # LP is not allowed as it is ambiguous - Linv = OnnxPow(LP[0], numpy.array([-0.5], dtype=dtype), - op_version=opv) - Linv.set_onnx_name_prefix('Linv') - - # diag = numpy.diag(Linv) - diag = OnnxMul( - OnnxEyeLike( - numpy.zeros((op.nf_, op.nf_), dtype=numpy.int64), - k=0, op_version=opv), - Linv, op_version=opv) - diag.set_onnx_name_prefix('diag') - - # root = P @ diag @ P.transpose() - trv = OnnxTranspose(LP[1], op_version=opv) - coef_left = OnnxMatMul(LP[1], diag, op_version=opv) - coef_left.set_onnx_name_prefix('coef_left') - coef = OnnxMatMul(coef_left, trv, op_version=opv) - coef.set_onnx_name_prefix('coef') - - # Same part as before. - Y = OnnxMatMul(X2, coef, op_version=opv, output_names=out[:1]) - Y.set_onnx_name_prefix('Y') - - # The last line specifies the final output. - # Every node involved in the computation is added to the ONNX - # graph at this stage. - Y.add_to(scope, container) - - -################################### -# Runtime for Eig -# ^^^^^^^^^^^^^^^ -# -# Here comes the new part. The python runtime does not -# implement any runtime for *Eig*. We need to tell the runtime -# to compute eigen values and vectors every time operator *Eig* -# is called. That means implementing two methods, -# one to compute, one to infer the shape of the results. -# The first one is mandatory, the second one can return an -# empty shape if it depends on the inputs. If it is known, -# the runtime may be able to optimize the computation, -# by reducing allocation for example. - -class OpEig(OpRunCustom): - - op_name = 'Eig' # operator name - atts = {'eigv': True} # operator parameters - - def __init__(self, onnx_node, desc=None, **options): - # constructor, every parameter is added a member - OpRunCustom.__init__(self, onnx_node, desc=desc, - expected_attributes=OpEig.atts, - **options) - - def run(self, x, **kwargs): - # computation - if self.eigv: - return numpy.linalg.eig(x) - return (numpy.linalg.eigvals(x), ) - - -######################################## -# Registration -# ^^^^^^^^^^^^ - - -update_registered_converter( - LiveDecorrelateTransformer, "SklearnLiveDecorrelateTransformer", - live_decorrelate_transformer_shape_calculator, - live_decorrelate_transformer_converter) - -####################################### -# Final example -# +++++++++++++ - - -data = load_iris() -X = data.data - -dec = LiveDecorrelateTransformer() -dec.fit(X) - -onx = to_onnx(dec, X.astype(numpy.float32), target_opset=17) - -register_operator(OpEig, name='Eig', overwrite=False) - -oinf = ReferenceEvaluator(onx) - -exp = dec.transform(X.astype(numpy.float32)) -got = oinf.run(None, {'X': X.astype(numpy.float32)})[0] - - -def diff(p1, p2): - p1 = p1.ravel() - p2 = p2.ravel() - d = numpy.abs(p2 - p1) - return d.max(), (d / numpy.abs(p1)).max() - - -print(diff(exp, got)) - -############################################# -# It works! diff --git a/docs/tutorial/plot_qextend_onnxruntime.py b/docs/tutorial/plot_qextend_onnxruntime.py deleted file mode 100644 index c5b4b7ef0..000000000 --- a/docs/tutorial/plot_qextend_onnxruntime.py +++ /dev/null @@ -1,21 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 - -""" -Fast runtime with onnxruntime -============================= - -:epkg:`ONNX operators` does not contain operator -from :epkg:`numpy`. There is no operator for -`solve `_ but this one -is needed to implement the prediction function -of model :epkg:`NMF`. The converter can be written -including a new ONNX operator but then it requires a -runtime for it to be tested. Example -:ref:`l-extend-python-runtime` shows how to do that -with :epkg:`mlprodict`. Doing the same with -:epkg:`onnxruntime` is more ambitious as it requires -C++... - -*to be continued* -""" diff --git a/docs/tutorial_3_new_operator.rst b/docs/tutorial_3_new_operator.rst deleted file mode 100644 index ed1e63e86..000000000 --- a/docs/tutorial_3_new_operator.rst +++ /dev/null @@ -1,25 +0,0 @@ -.. SPDX-License-Identifier: Apache-2.0 - - -Extend ONNX, extend runtime -=========================== - -Existing converters assume it is possible to convert -a model with the current list of :epkg:`ONNX operators`. -This list is growing at every version but it may happen -a new node is needed. It could be added to ONNX specifications, -it requires a new release, but that's not mandatory. -New nodes can easily be created by using a different domain. -A domain defines a set of operators, there are currently two -officially supported domains: :epkg:`ONNX operators` and -:epkg:`ONNX ML operators`. Custom domains can be used. -Once this new node is defined, a converter can use it. -That leaves the last issue: the runtime must be aware -of the implementation attached to this new node. -That's the difficult part. - -.. toctree:: - :maxdepth: 1 - - auto_tutorial/plot_pextend_python_runtime - auto_tutorial/plot_qextend_onnxruntime From bbc4c838d9c3f237700be786914b76dc2a9ee67f Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 15:34:34 +0200 Subject: [PATCH 08/15] doc Signed-off-by: Xavier Dupre --- docs/tutorial/plot_abegin_convert_pipeline.py | 9 --------- docs/tutorial/plot_dbegin_options.py | 7 ------- docs/tutorial/plot_fbegin_investigate.py | 8 -------- 3 files changed, 24 deletions(-) diff --git a/docs/tutorial/plot_abegin_convert_pipeline.py b/docs/tutorial/plot_abegin_convert_pipeline.py index 665f9fabb..b95e15dbf 100644 --- a/docs/tutorial/plot_abegin_convert_pipeline.py +++ b/docs/tutorial/plot_abegin_convert_pipeline.py @@ -115,12 +115,3 @@ def diff(p1, p2): pred_pyrt = oinf.run(None, {'X': X_test.astype(numpy.float32)})[0] print(diff(pred_skl, pred_pyrt)) - -############################# -# Final graph -# You may need to install graphviz from https://graphviz.org/download/ -# +++++++++++ - -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) diff --git a/docs/tutorial/plot_dbegin_options.py b/docs/tutorial/plot_dbegin_options.py index d884f4bce..d793fe255 100644 --- a/docs/tutorial/plot_dbegin_options.py +++ b/docs/tutorial/plot_dbegin_options.py @@ -92,13 +92,6 @@ oinf = ReferenceEvaluator(model_def) print(oinf) -################################## -# Visually. - -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) - ########################################## # We need to compare that kind of visualisation to diff --git a/docs/tutorial/plot_fbegin_investigate.py b/docs/tutorial/plot_fbegin_investigate.py index 80a62b7f0..a8ce9b99a 100644 --- a/docs/tutorial/plot_fbegin_investigate.py +++ b/docs/tutorial/plot_fbegin_investigate.py @@ -113,11 +113,3 @@ # This way is usually better if you need to investigate # issues within the code of the runtime for an operator. -# -################################# -# Final graph -# +++++++++++ - -ax = plot_graphviz(oinf.to_dot()) -ax.get_xaxis().set_visible(False) -ax.get_yaxis().set_visible(False) From d34407871357a42f4bf1bb8ea7e887e8d8b5d1e6 Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 15:51:43 +0200 Subject: [PATCH 09/15] docs Signed-off-by: Xavier Dupre --- docs/tutorial/plot_dbegin_options.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/docs/tutorial/plot_dbegin_options.py b/docs/tutorial/plot_dbegin_options.py index d793fe255..1d0468aa6 100644 --- a/docs/tutorial/plot_dbegin_options.py +++ b/docs/tutorial/plot_dbegin_options.py @@ -93,15 +93,6 @@ print(oinf) -########################################## -# We need to compare that kind of visualisation to -# what it would give with operator *ZipMap*. - -model_def = to_onnx(clr, X_train.astype(numpy.float32)) -oinf = ReferenceEvaluator(model_def) -print(oinf) - - ####################################### # Using function *id* has one flaw: it is not pickable. # It is just better to use strings. From 59f494aa2146af75138bcf0bebb9d20d39b30921 Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 14:58:05 +0000 Subject: [PATCH 10/15] add cpu provider Signed-off-by: Xavier Dupre --- docs/tutorial/plot_bbegin_measure_time.py | 5 +++-- docs/tutorial/plot_dbegin_options_list.py | 6 ++++-- docs/tutorial/plot_icustom_converter.py | 6 ++++-- 3 files changed, 11 insertions(+), 6 deletions(-) diff --git a/docs/tutorial/plot_bbegin_measure_time.py b/docs/tutorial/plot_bbegin_measure_time.py index fa1eb6073..3acabe3e4 100644 --- a/docs/tutorial/plot_bbegin_measure_time.py +++ b/docs/tutorial/plot_bbegin_measure_time.py @@ -85,7 +85,8 @@ onx = to_onnx(ereg, X_train[:1].astype(numpy.float32), target_opset=14) -sess = InferenceSession(onx.SerializeToString()) +sess = InferenceSession(onx.SerializeToString(), + providers=["CPUExecutionProvider"]) oinf = ReferenceEvaluator(onx) obs = [] @@ -109,7 +110,7 @@ # ReferenceEvaluator context = {"oinf": oinf, 'X': X_test[:batch_size].astype(numpy.float32)} mt2 = measure_time( - "oinf.run({'X': X})['variable']", context, div_by_number=True, + "oinf.run(None, {'X': X})[0]", context, div_by_number=True, number=10, repeat=repeat) mt['pyrt'] = mt2['average'] / mt['size'] diff --git a/docs/tutorial/plot_dbegin_options_list.py b/docs/tutorial/plot_dbegin_options_list.py index 40ded3812..b6c02fc7e 100644 --- a/docs/tutorial/plot_dbegin_options_list.py +++ b/docs/tutorial/plot_dbegin_options_list.py @@ -42,7 +42,8 @@ model, X_train[:1].astype(numpy.float32), options={id(model): {'score_samples': True}}, target_opset=12) -sess = InferenceSession(model_onnx.SerializeToString()) +sess = InferenceSession(model_onnx.SerializeToString(), + providers=["CPUExecutionProvider"]) xt = X_test[:5].astype(numpy.float32) print(model.score_samples(xt)) @@ -62,7 +63,8 @@ options={id(model): {'score_samples': True}}, black_op={'ReduceLogSumExp'}, target_opset=12) -sess2 = InferenceSession(model_onnx2.SerializeToString()) +sess2 = InferenceSession(model_onnx2.SerializeToString(), + providers=["CPUExecutionProvider"]) xt = X_test[:5].astype(numpy.float32) print(model.score_samples(xt)) diff --git a/docs/tutorial/plot_icustom_converter.py b/docs/tutorial/plot_icustom_converter.py index 347c7aea0..9db907adf 100644 --- a/docs/tutorial/plot_icustom_converter.py +++ b/docs/tutorial/plot_icustom_converter.py @@ -202,7 +202,8 @@ def decorrelate_transformer_converter(scope, operator, container): onx = to_onnx(dec, X.astype(numpy.float32)) -sess = InferenceSession(onx.SerializeToString()) +sess = InferenceSession(onx.SerializeToString(), + providers=["CPUExecutionProvider"]) exp = dec.transform(X.astype(numpy.float32)) got = sess.run(None, {'X': X.astype(numpy.float32)})[0] @@ -222,7 +223,8 @@ def diff(p1, p2): onx = to_onnx(dec, X.astype(numpy.float64)) -sess = InferenceSession(onx.SerializeToString()) +sess = InferenceSession(onx.SerializeToString(), + providers=["CPUExecutionProvider"]) exp = dec.transform(X.astype(numpy.float64)) got = sess.run(None, {'X': X.astype(numpy.float64)})[0] From 48fd05ccdcd53ffadcb2bebe61147ebf832398b9 Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 16:40:14 +0000 Subject: [PATCH 11/15] documentation Signed-off-by: Xavier Dupre --- docs/tutorial/plot_fbegin_investigate.py | 8 ++++---- tests/test_sklearn_double_tensor_type_cls.py | 6 +++++- tests/test_sklearn_glm_regressor_converter.py | 4 ++-- 3 files changed, 11 insertions(+), 7 deletions(-) diff --git a/docs/tutorial/plot_fbegin_investigate.py b/docs/tutorial/plot_fbegin_investigate.py index a8ce9b99a..024af5d35 100644 --- a/docs/tutorial/plot_fbegin_investigate.py +++ b/docs/tutorial/plot_fbegin_investigate.py @@ -102,14 +102,14 @@ onx = to_onnx(pipe, X[:1].astype(numpy.float32), target_opset=17) -oinf = ReferenceEvaluator(onx) -oinf.run(None, {'X': X[:2].astype(numpy.float32)}, verbose=1) +oinf = ReferenceEvaluator(onx, verbose=1) +oinf.run(None, {'X': X[:2].astype(numpy.float32)}) ################################### # And to get a sense of the intermediate results. -oinf.run({'X': X[:2].astype(numpy.float32)}, - verbose=3, fLOG=print) +oinf = ReferenceEvaluator(onx, verbose=3) +oinf.run({'X': X[:2].astype(numpy.float32)}) # This way is usually better if you need to investigate # issues within the code of the runtime for an operator. diff --git a/tests/test_sklearn_double_tensor_type_cls.py b/tests/test_sklearn_double_tensor_type_cls.py index 86264dfb5..9e91e5d28 100644 --- a/tests/test_sklearn_double_tensor_type_cls.py +++ b/tests/test_sklearn_double_tensor_type_cls.py @@ -3,6 +3,7 @@ import unittest import packaging.version as pv import numpy as np +from sklearn import __version__ as skl_version from sklearn.calibration import CalibratedClassifierCV from sklearn.exceptions import ConvergenceWarning from sklearn.ensemble import BaggingClassifier @@ -46,6 +47,9 @@ ORT_VERSION = '1.7.0' onnx_version = ".".join(onnx_version.split('.')[:2]) +LOG_LOSS = ("log_loss" if pv.Version(skl_version) >= pv.Version("1.1") + else "log") + class TestSklearnDoubleTensorTypeClassifier(unittest.TestCase): @@ -134,7 +138,7 @@ def test_modelsgd_64(self): @ignore_warnings(category=warnings_to_skip) def test_modelsgdlog_64(self): self._common_classifier( - [lambda: SGDClassifier(loss='log_loss', random_state=32)], + [lambda: SGDClassifier(loss=LOG_LOSS, random_state=32)], "SGDClassifierLog") @unittest.skipIf( diff --git a/tests/test_sklearn_glm_regressor_converter.py b/tests/test_sklearn_glm_regressor_converter.py index 8df67187b..1065b3acf 100644 --- a/tests/test_sklearn_glm_regressor_converter.py +++ b/tests/test_sklearn_glm_regressor_converter.py @@ -646,7 +646,7 @@ def test_model_ransac_regressor_default(self): def test_model_ransac_regressor_mlp(self): model, X = fit_regression_model( linear_model.RANSACRegressor( - estimator=MLPRegressor(solver='sgd', max_iter=20), + MLPRegressor(solver='sgd', max_iter=20), min_samples=5)) model_onnx = convert_sklearn( model, "ransac regressor", @@ -661,7 +661,7 @@ def test_model_ransac_regressor_mlp(self): def test_model_ransac_regressor_tree(self): model, X = fit_regression_model( linear_model.RANSACRegressor( - estimator=GradientBoostingRegressor(), + GradientBoostingRegressor(), min_samples=5)) model_onnx = convert_sklearn( model, "ransac regressor", From ebf1f7830cf1a6673c16eb25592dc51c44668015 Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Mon, 3 Jul 2023 18:57:35 +0200 Subject: [PATCH 12/15] fix documentation Signed-off-by: Xavier Dupre --- docs/tutorial/plot_fbegin_investigate.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial/plot_fbegin_investigate.py b/docs/tutorial/plot_fbegin_investigate.py index 024af5d35..422be0798 100644 --- a/docs/tutorial/plot_fbegin_investigate.py +++ b/docs/tutorial/plot_fbegin_investigate.py @@ -109,7 +109,7 @@ # And to get a sense of the intermediate results. oinf = ReferenceEvaluator(onx, verbose=3) -oinf.run({'X': X[:2].astype(numpy.float32)}) +oinf.run(None, {'X': X[:2].astype(numpy.float32)}) # This way is usually better if you need to investigate # issues within the code of the runtime for an operator. From 9a37b063f30356a698f4dccf5bef2c5702516dda Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Tue, 4 Jul 2023 10:36:56 +0200 Subject: [PATCH 13/15] doc Signed-off-by: Xavier Dupre --- docs/tutorial/plot_ebegin_float_double.py | 42 ----------------------- 1 file changed, 42 deletions(-) diff --git a/docs/tutorial/plot_ebegin_float_double.py b/docs/tutorial/plot_ebegin_float_double.py index 2cf99f7bd..e95e979b6 100644 --- a/docs/tutorial/plot_ebegin_float_double.py +++ b/docs/tutorial/plot_ebegin_float_double.py @@ -243,45 +243,3 @@ def diff(p1, p2): # the computation type when a pipeline includes a discontinuous # function. It is better to keep the same types all along # before using a decision tree. -# -# Sledgehammer -# ++++++++++++ -# -# The idea here is to always train the next step based -# on ONNX outputs. That way, every step of the pipeline -# is trained based on ONNX output. -# -# * Trains the first step. -# * Converts the step into ONNX -# * Computes ONNX outputs. -# * Trains the second step on these outputs. -# * Converts the second step into ONNX. -# * Merges it with the first step. -# * Computes ONNX outputs of the merged two first steps. -# * ... -# -# It is implemented in -# class :epkg:`OnnxPipeline`. - - -model_onx = OnnxPipeline([ - ('scaler', StandardScaler()), - ('dt', DecisionTreeRegressor(max_depth=max_depth)) -]) - -model_onx.fit(Xi_train, yi_train) - -############################################# -# By using opset 17 and opset 3 for domain ai.onnx.ml, the tree thresholds -# can be stored as double and not float anymore. That lowerss the discrepancies -# even if the outputs are still float. - -onx4 = to_onnx(model_onx, Xi_train[:1].astype(numpy.float32), - target_opset=17) - -sess4 = InferenceSession(onx4.SerializeToString()) - -skl4 = model_onx.predict(X32) -ort4 = sess4.run(None, {'X': X32})[0] - -print(diff(skl4, ort4)) From adf0a20f05a501d88fc0af8f2f739dc73e1d405a Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Tue, 4 Jul 2023 11:49:37 +0200 Subject: [PATCH 14/15] remove unnecessary examples Signed-off-by: Xavier Dupre --- .../tutorial/plot_gbegin_transfer_learning.py | 255 ------------------ docs/tutorial_1_simple.rst | 1 - 2 files changed, 256 deletions(-) delete mode 100644 docs/tutorial/plot_gbegin_transfer_learning.py diff --git a/docs/tutorial/plot_gbegin_transfer_learning.py b/docs/tutorial/plot_gbegin_transfer_learning.py deleted file mode 100644 index 415d6e266..000000000 --- a/docs/tutorial/plot_gbegin_transfer_learning.py +++ /dev/null @@ -1,255 +0,0 @@ -# SPDX-License-Identifier: Apache-2.0 - -""" -Transfer Learning with ONNX -=========================== - -.. index:: transfer learning, deep learning - -Transfer learning is common with deep learning. -A deep learning model is used as preprocessing before -the output is sent to a final classifier or regressor. -It is not quite easy in this case to mix framework, -:epkg:`scikit-learn` with :epkg:`pytorch` -(or :epkg:`skorch`), the Keras API for Tensorflow, -`tf.keras.wrappers.scikit_learn -`_. Every combination -requires work. ONNX reduces the number of platforms to -support. Once the model is converted into ONNX, -it can be inserted in any :epkg:`scikit-learn` pipeline. - -Retrieve and load a model -+++++++++++++++++++++++++ - -We download one model from the :epkg:`ONNX Zoo` but the model -could be trained and produced by another converter library. -""" -import sys -from io import BytesIO -import onnx -from sklearn.decomposition import PCA -from sklearn.pipeline import Pipeline -from mlinsights.plotting.gallery import plot_gallery_images -import matplotlib.pyplot as plt -from skl2onnx.tutorial.imagenet_classes import class_names -import numpy -from PIL import Image -from onnxruntime import InferenceSession -from onnxruntime.capi.onnxruntime_pybind11_state import InvalidArgument -import os -import urllib.request - - -def download_file(url, name, min_size): - if not os.path.exists(name): - print("download '%s'" % url) - with urllib.request.urlopen(url) as u: - content = u.read() - if len(content) < min_size: - raise RuntimeError( - "Unable to download '{}' due to\n{}".format( - url, content)) - print("downloaded %d bytes." % len(content)) - with open(name, "wb") as f: - f.write(content) - else: - print("'%s' already downloaded" % name) - - -model_name = "squeezenet1.1-7.onnx" -url_name = ("https://github.com/onnx/models/raw/main/vision/" - "classification/squeezenet/model") -url_name += "/" + model_name -try: - download_file(url_name, model_name, 100000) -except RuntimeError as e: - print(e) - sys.exit(1) - - -################################################ -# Loading the ONNX file and use it on one image. - -sess = InferenceSession(model_name) - -for inp in sess.get_inputs(): - print(inp) - -##################################### -# The model expects a series of images of size -# `[3, 224, 224]`. - -########################################## -# Classifying an image -# ++++++++++++++++++++ - -url = ("https://upload.wikimedia.org/wikipedia/commons/d/d2/" - "East_Coker_elm%2C_2.jpg") -img = "East_Coker_elm.jpg" -download_file(url, img, 100000) - -im0 = Image.open(img) -im = im0.resize((224, 224)) -# im.show() - -###################################### -# Image to numpy and predection. - - -def im2array(im): - X = numpy.asarray(im) - X = X.transpose(2, 0, 1) - X = X.reshape(1, 3, 224, 224) - return X - - -X = im2array(im) -out = sess.run(None, {'data': X.astype(numpy.float32)}) -out = out[0] - -print(out[0, :5]) - -##################################### -# Interpretation - - -res = list(sorted((r, class_names[i]) for i, r in enumerate(out[0]))) -print(res[-5:]) - -########################################## -# Classifying more images -# +++++++++++++++++++++++ -# -# The initial image is rotated, -# the answer is changing. - -angles = [a * 2. for a in range(-6, 6)] -imgs = [(angle, im0.rotate(angle).resize((224, 224))) - for angle in angles] - - -def classify(imgs): - labels = [] - for angle, img in imgs: - X = im2array(img) - probs = sess.run(None, {'data': X.astype(numpy.float32)})[0] - pl = list(sorted( - ((r, class_names[i]) for i, r in enumerate(probs[0])), - reverse=True)) - labels.append((angle, pl)) - return labels - - -climgs = classify(imgs) -for angle, res in climgs: - print("angle={} - {}".format(angle, res[:5])) - - -plot_gallery_images([img[1] for img in imgs], - [img[1][0][1][:15] for img in climgs]) - -######################################### -# Transfer learning in a pipeline -# +++++++++++++++++++++++++++++++ -# -# The proposed transfer learning consists -# using a PCA to projet the probabilities -# on a graph. - - -with open(model_name, 'rb') as f: - model_bytes = f.read() - -pipe = Pipeline(steps=[ - ('deep', OnnxTransformer( - model_bytes, runtime='onnxruntime1', change_batch_size=0)), - ('pca', PCA(2)) -]) - -X_train = numpy.vstack( - [im2array(img) for _, img in imgs]).astype(numpy.float32) -pipe.fit(X_train) - -proj = pipe.transform(X_train) -print(proj) - -########################################### -# Graph for the PCA -# ----------------- - -fig, ax = plt.subplots(1, 1, figsize=(5, 5)) -ax.plot(proj[:, 0], proj[:, 1], 'o') -ax.set_title("Projection of classification probabilities") -text = ["%1.0f-%s" % (el[0], el[1][0][1]) for el in climgs] -for label, x, y in zip(text, proj[:, 0], proj[:, 1]): - ax.annotate( - label, xy=(x, y), xytext=(-10, 10), fontsize=8, - textcoords='offset points', ha='right', va='bottom', - bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5), - arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')) - -########################################### -# Remove one layer at the end -# --------------------------- -# -# The last is often removed before the model is -# inserted in a pipeline. Let's see how to do that. -# First, we need the list of output for every node. - - -model_onnx = onnx.load(BytesIO(model_bytes)) -outputs = [] -for node in model_onnx.graph.node: - print(node.name, node.output) - outputs.extend(node.output) - -################################# -# We select one of the last one. - -selected = outputs[-3] -print("selected", selected) - -################################# -# And we tell *OnnxTransformer* to use that -# specific one and to flatten the output -# as the dimension is not a matrix. - - -pipe2 = Pipeline(steps=[ - ('deep', OnnxTransformer( - model_bytes, runtime='onnxruntime1', change_batch_size=0, - output_name=selected, reshape=True)), - ('pca', PCA(2)) -]) - -try: - pipe2.fit(X_train) -except InvalidArgument as e: - print("Unable to fit due to", e) - -####################################### -# We check that it is different. -# The following values are the shape of the -# PCA components. The number of column is the number -# of dimensions of the outputs of the transfered -# neural network. - -print(pipe.steps[1][1].components_.shape, - pipe2.steps[1][1].components_.shape) - -####################################### -# Graph again. - -proj2 = pipe2.transform(X_train) - -fig, ax = plt.subplots(1, 1, figsize=(5, 5)) -ax.plot(proj2[:, 0], proj2[:, 1], 'o') -ax.set_title("Second projection of classification probabilities") -text = ["%1.0f-%s" % (el[0], el[1][0][1]) for el in climgs] -for label, x, y in zip(text, proj2[:, 0], proj2[:, 1]): - ax.annotate( - label, xy=(x, y), xytext=(-10, 10), fontsize=8, - textcoords='offset points', ha='right', va='bottom', - bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5), - arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')) diff --git a/docs/tutorial_1_simple.rst b/docs/tutorial_1_simple.rst index a4ec0795f..e33ba4815 100644 --- a/docs/tutorial_1_simple.rst +++ b/docs/tutorial_1_simple.rst @@ -27,4 +27,3 @@ used in the ONNX graph. auto_tutorial/plot_gbegin_cst auto_tutorial/plot_gbegin_dataframe auto_tutorial/plot_gconverting - auto_tutorial/plot_gbegin_transfer_learning From 2d57e670dfeaf1809e3d38f8a59518a2ca40683e Mon Sep 17 00:00:00 2001 From: Xavier Dupre Date: Tue, 4 Jul 2023 12:57:44 +0200 Subject: [PATCH 15/15] fix wrong import Signed-off-by: Xavier Dupre --- docs/tutorial/plot_abegin_convert_pipeline.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/tutorial/plot_abegin_convert_pipeline.py b/docs/tutorial/plot_abegin_convert_pipeline.py index b95e15dbf..f41c5f5c1 100644 --- a/docs/tutorial/plot_abegin_convert_pipeline.py +++ b/docs/tutorial/plot_abegin_convert_pipeline.py @@ -28,7 +28,7 @@ from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline from skl2onnx import to_onnx -from onxn.reference import ReferenceEvaluator +from onnx.reference import ReferenceEvaluator X, y = load_diabetes(return_X_y=True)

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